Udacity Deep Learning Foundation v1 | 980D687AC1263D7CBDD6CD5EAAC2CB239FAF8A7E

Udacity Deep Learning Foundation v1

Name	Udacity - Deep Learning Foundation v1.0.0	DOWNLOAD Copy Link
Total Size	6.0 GB
Total Files	3747
Hash	980D687AC1263D7CBDD6CD5EAAC2CB239FAF8A7E

/Part 02-Module 03-Lesson 01_MiniFlow/
07. Pixels are Features!-qE5YYXtPq9U.en-US.vtt	0.1 KB
07. Pixels are Features!-qE5YYXtPq9U.pt-BR.vtt	0.1 KB
01. Miniflow Introduction-Nqp_UifEwt0.zh-CN.vtt	1.1 KB
01. Miniflow Introduction-Nqp_UifEwt0.en-US.vtt	1.2 KB
01. Miniflow Introduction-Nqp_UifEwt0.pt-BR.vtt	1.3 KB
index.html	4.6 KB
01. Welcome to MiniFlow.html	6.2 KB
15. Outro.html	6.3 KB
10. Cost Solution.html	7.4 KB
02. Graphs.html	10.3 KB
03. MiniFlow Architecture.html	10.5 KB
11. Gradient Descent.html	12.1 KB
05. Forward Propagation Solution.html	13.5 KB
06. Learning and Loss.html	13.6 KB
04. Forward Propagation.html	15.3 KB
08. Sigmoid Function.html	17.7 KB
09. Cost.html	18.2 KB
07. Linear Transform.html	19.9 KB
14. SGD Solution.html	20.0 KB
13. Stochastic Gradient Descent.html	24.6 KB
12. Backpropagation.html	37.0 KB
07. Pixels are Features!-qE5YYXtPq9U.mp4	1.1 MB
01. Miniflow Introduction-Nqp_UifEwt0.mp4	8.4 MB
/Part 02-Module 05-Lesson 02_Convolutional Networks/
01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt	0.3 KB
01. Intro to CNNs-B61jxZ4rkMs.en.vtt	0.3 KB
01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt	0.3 KB
01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt	0.3 KB
01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt	0.5 KB
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt	0.6 KB
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt	0.6 KB
02. Color-Question-BdQccpMwk80.zh-CN.vtt	0.6 KB
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt	0.7 KB
02. Color-Question-BdQccpMwk80.pt-BR.vtt	0.7 KB
02. Color-Question-BdQccpMwk80.en.vtt	0.7 KB
08. Convolutions Cont.-utOv-BKI_vo.zh-CN.vtt	0.9 KB
08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt	1.0 KB
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.zh-CN.vtt	1.0 KB
08. Convolutions Cont.-utOv-BKI_vo.en.vtt	1.1 KB
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.en.vtt	1.1 KB
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.pt-BR.vtt	1.1 KB
29. Inception Module-SlTm03bEOxA.zh-CN.vtt	1.4 KB
28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt	1.5 KB
29. Inception Module-SlTm03bEOxA.pt-BR.vtt	1.6 KB
29. Inception Module-SlTm03bEOxA.en.vtt	1.6 KB
28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt	1.7 KB
28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt	1.9 KB
03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt	2.2 KB
03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt	2.3 KB
03. Statistical Invariance-0Hr5YwUUhr0.en.vtt	2.5 KB
18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt	3.0 KB
18. Explore the Design Space-FG7M9tWH2nQ.en.vtt	3.5 KB
18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt	3.7 KB
04. Convolutional Networks-ISHGyvsT0QY.zh-CN.vtt	3.9 KB
04. Convolutional Networks-ISHGyvsT0QY.en.vtt	4.5 KB
04. Convolutional Networks-ISHGyvsT0QY.pt-BR.vtt	4.6 KB
index.html	6.5 KB
25. Solution Pooling Practice.html	8.5 KB
27. Solution Average Pooling.html	8.5 KB
28. 1x1 Convolutions.html	8.6 KB
29. Inception Module.html	8.6 KB
13. Solution Number of Parameters.html	8.6 KB
04. Convolutional Networks.html	8.6 KB
03. Statistical Invariance.html	8.6 KB
18. Explore The Design Space.html	8.6 KB
15. Solution Parameter Sharing.html	8.7 KB
01. Intro To CNNs.html	8.8 KB
23. Solution Pooling Mechanics.html	8.9 KB
08. Convolutions continued.html	8.9 KB
21. Solution Pooling Intuition.html	8.9 KB
26. Quiz Average Pooling.html	9.0 KB
35. CNNs - Additional Resources.html	9.1 KB
02. Color.html	9.1 KB
24. Quiz Pooling Practice.html	9.2 KB
20. Quiz Pooling Intuition.html	9.2 KB
34. Solution TensorFlow Pooling Layer.html	9.4 KB
12. Quiz Number of Parameters.html	9.6 KB
10. Quiz Convolution Output Shape.html	9.7 KB
14. Quiz Parameter Sharing.html	9.8 KB
22. Quiz Pooling Mechanics.html	9.8 KB
32. Solution TensorFlow Convolution Layer.html	9.9 KB
11. Solution Convolution Output Shape.html	10.2 KB
33. TensorFlow Pooling Layer.html	10.5 KB
17. TensorFlow Convolution Layer.html	10.6 KB
19. TensorFlow Max Pooling.html	11.0 KB
07. Feature Map Sizes.html	11.2 KB
31. TensorFlow Convolution Layer.html	12.0 KB
09. Parameters.html	14.1 KB
05. Intuition.html	14.1 KB
06. Filters.html	14.8 KB
16. Visualizing CNNs.html	15.9 KB
30. Convolutional Network in TensorFlow.html	19.2 KB
07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4	725.9 KB
02. Color-Question-BdQccpMwk80.mp4	839.5 KB
07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4	1.2 MB
01. Intro to CNNs-B61jxZ4rkMs.mp4	1.7 MB
08. Convolutions Cont.-utOv-BKI_vo.mp4	1.7 MB
03. Statistical Invariance-0Hr5YwUUhr0.mp4	2.3 MB
29. Inception Module-SlTm03bEOxA.mp4	2.7 MB
28. 1x1 Convolutions-Zmzgerm6SjA.mp4	3.3 MB
18. Explore the Design Space-FG7M9tWH2nQ.mp4	5.2 MB
04. Convolutional Networks-ISHGyvsT0QY.mp4	8.8 MB
/Part 07-Module 01-Lesson 01_Introduction to Neural Networks/
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt	0.4 KB
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt	0.4 KB
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt	0.4 KB
15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt	0.5 KB
16. Quiz - Softmax-NNoezNnAMTY.en.vtt	0.5 KB
16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt	0.5 KB
21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt	0.5 KB
16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt	0.5 KB
15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt	0.6 KB
15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt	0.6 KB
30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt	0.6 KB
21. Formula For Cross 1-qvr_ego_d6w.en.vtt	0.6 KB
30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt	0.6 KB
30. Non-Linear Data-F7ZiE8PQiSc.en.vtt	0.6 KB
21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt	0.7 KB
13. Error Functions-YfUUunxWIJw.zh-CN.vtt	0.7 KB
13. Error Functions-YfUUunxWIJw.en.vtt	0.8 KB
13. Error Functions-YfUUunxWIJw.pt-BR.vtt	0.8 KB
19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt	0.8 KB
19. Quiz - Cross 1--xxrisIvD0E.en.vtt	0.9 KB
19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt	0.9 KB
09. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt	1.0 KB
09. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt	1.0 KB
09. XOR Perceptron-TF83GfjYLdw.en.vtt	1.0 KB
31. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt	1.1 KB
29. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt	1.2 KB
08. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt	1.2 KB
08. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt	1.3 KB
31. Non-Linear Models-HWuBKCZsCo8.en.vtt	1.3 KB
29. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt	1.3 KB
29. Continuous Perceptrons-07-JJ-aGEfM.en.vtt	1.4 KB
08. Why Neural Networks-zAkzOZntK6Y.en.vtt	1.4 KB
31. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt	1.4 KB
34. Chain Rule-YAhIBOnbt54.zh-CN.vtt	1.5 KB
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt	1.5 KB
18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt	1.5 KB
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt	1.5 KB
12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt	1.5 KB
12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt	1.6 KB
04. Classification Example-46PywnGa_cQ.pt-BR.vtt	1.6 KB
18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt	1.7 KB
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt	1.7 KB
18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt	1.7 KB
04. Classification Example-46PywnGa_cQ.zh-CN.vtt	1.7 KB
34. Chain Rule-YAhIBOnbt54.en.vtt	1.7 KB
33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt	1.7 KB
34. Chain Rule-YAhIBOnbt54.pt-BR.vtt	1.8 KB
04. Classification Example-46PywnGa_cQ.en.vtt	1.8 KB
12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt	1.8 KB
32. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt	1.9 KB
33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt	2.0 KB
17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt	2.1 KB
17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt	2.1 KB
19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt	2.1 KB
32. Multiclass Classification-uNTtvxwfox0.en.vtt	2.1 KB
32. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt	2.2 KB
33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt	2.2 KB
25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt	2.3 KB
17. One-Hot Encoding-AePvjhyvsBo.en.vtt	2.3 KB
19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt	2.3 KB
19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt	2.4 KB
16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt	2.4 KB
10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt	2.4 KB
03. Classsification Example-Dh625piH7Z0.zh-CN.vtt	2.4 KB
06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt	2.4 KB
10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt	2.5 KB
09. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt	2.5 KB
03. Classsification Example-Dh625piH7Z0.pt-BR.vtt	2.6 KB
16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt	2.6 KB
25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt	2.6 KB
16. DL 18 S Softmax-n8S-v_LCTms.en.vtt	2.7 KB
25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt	2.7 KB
10. Perceptron Algorithm--zhTROHtscQ.en.vtt	2.7 KB
06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt	2.7 KB
03. Classsification Example-Dh625piH7Z0.en.vtt	2.8 KB
32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt	2.8 KB
02. Introduction-tn-CrUTkCUc.zh-CN.vtt	2.9 KB
34. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt	3.0 KB
06. 09 Higher Dimensions-eBHunImDmWw.en.vtt	3.0 KB
09. AND And OR Perceptrons-45K5N0P9wJk.en.vtt	3.1 KB
32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt	3.1 KB
32. Layers-pg99FkXYK0M.zh-CN.vtt	3.1 KB
02. Introduction-tn-CrUTkCUc.pt-BR.vtt	3.2 KB
09. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt	3.2 KB
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt	3.4 KB
02. Introduction-tn-CrUTkCUc.en.vtt	3.4 KB
32. Layers-pg99FkXYK0M.pt-BR.vtt	3.4 KB
32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt	3.4 KB
05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt	3.4 KB
32. Layers-pg99FkXYK0M.en.vtt	3.5 KB
34. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt	3.5 KB
34. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt	3.5 KB
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt	3.5 KB
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt	3.6 KB
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt	3.7 KB
05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt	3.8 KB
18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt	3.8 KB
24. Gradient Descent-rhVIF-nigrY.en.vtt	3.9 KB
05. Linear Boundaries-X-uMlsBi07k.en.vtt	3.9 KB
24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt	4.1 KB
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt	4.1 KB
20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt	4.2 KB
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt	4.2 KB
23. Error Function-V5kkHldUlVU.zh-CN.vtt	4.2 KB
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt	4.3 KB
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt	4.3 KB
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt	4.4 KB
16. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt	4.5 KB
18. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt	4.5 KB
18. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt	4.6 KB
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt	4.7 KB
32. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt	4.7 KB
15. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt	4.8 KB
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt	4.8 KB
20. Cross Entropy 1-iREoPUrpXvE.en.vtt	4.9 KB
23. Error Function-V5kkHldUlVU.en.vtt	5.0 KB
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt	5.1 KB
20. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt	5.1 KB
34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt	5.2 KB
16. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt	5.2 KB
23. Error Function-V5kkHldUlVU.pt-BR.vtt	5.3 KB
32. Combinando modelos-Boy3zHVrWB4.en.vtt	5.4 KB
32. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt	5.4 KB
33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt	5.5 KB
16. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt	5.5 KB
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt	5.7 KB
15. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt	5.8 KB
15. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt	5.8 KB
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt	6.0 KB
34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt	6.3 KB
33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt	6.3 KB
index.html	6.5 KB
34. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt	6.5 KB
34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt	6.7 KB
21. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt	6.8 KB
33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt	6.9 KB
34. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt	7.3 KB
34. Backpropagation V2-1SmY3TZTyUk.en.vtt	7.4 KB
21. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt	8.0 KB
21. CrossEntropy V1-1BnhC6e0TFw.en.vtt	8.2 KB
14. Error Functions-jfKShxGAbok.zh-CN.vtt	8.6 KB
02. Introduction.html	8.6 KB
37. Outro.html	8.6 KB
30. Non-linear Data.html	8.6 KB
13. Error Functions.html	8.6 KB
17. One-Hot Encoding.html	8.6 KB
31. Non-Linear Models.html	8.6 KB
12. Non-Linear Regions.html	8.6 KB
08. Why Neural Networks.html	8.6 KB
29. Continuous Perceptrons.html	8.6 KB
04. Classification Problems 2.html	8.6 KB
25. Logistic Regression Algorithm.html	8.7 KB
01. Instructor.html	8.7 KB
20. Cross-Entropy 1.html	8.8 KB
28. Perceptron vs Gradient Descent.html	9.0 KB
27. Notebook Gradient Descent.html	9.0 KB
36. Notebook Analyzing Student Data.html	9.0 KB
05. Linear Boundaries.html	9.3 KB
14. Error Functions-jfKShxGAbok.pt-BR.vtt	9.4 KB
35. Pre-Lab Analyzing Student Data.html	9.5 KB
07. Perceptrons.html	9.7 KB
22. Multi-Class Cross Entropy.html	9.7 KB
14. Error Functions-jfKShxGAbok.en.vtt	9.7 KB
06. Higher Dimensions.html	9.7 KB
03. Classification Problems 1.html	9.7 KB
33. Feedforward.html	9.9 KB
14. Log-loss Error Function.html	10.1 KB
23. Logistic Regression.html	10.3 KB
26. Pre-Lab Gradient Descent.html	10.4 KB
19. Maximizing Probabilities.html	10.4 KB
15. Discrete vs Continuous.html	10.8 KB
18. Maximum Likelihood.html	10.8 KB
21. Cross-Entropy 2.html	11.2 KB
10. Perceptron Trick.html	12.4 KB
34. Backpropagation.html	12.8 KB
16. Softmax.html	12.9 KB
32. Neural Network Architecture.html	13.2 KB
11. Perceptron Algorithm.html	16.2 KB
24. Gradient Descent.html	16.5 KB
09. Perceptrons as Logical Operators.html	19.5 KB
10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4	266.3 KB
09. XOR Perceptron-TF83GfjYLdw.mp4	969.7 KB
08. Why Neural Networks-zAkzOZntK6Y.mp4	1.0 MB
31. Non-Linear Models-HWuBKCZsCo8.mp4	1.2 MB
29. Continuous Perceptrons-07-JJ-aGEfM.mp4	1.2 MB
12. Non-Linear Regions-B8UrWnHh1Wc.mp4	1.4 MB
34. Chain Rule-YAhIBOnbt54.mp4	1.5 MB
23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4	1.6 MB
04. Classification Example-46PywnGa_cQ.mp4	1.7 MB
17. One-Hot Encoding-AePvjhyvsBo.mp4	1.7 MB
33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4	1.8 MB
16. Quiz - Softmax-NNoezNnAMTY.mp4	1.8 MB
19. Quiz Cross Entropy-njq6bYrPqSU.mp4	2.0 MB
32. Multiclass Classification-uNTtvxwfox0.mp4	2.0 MB
10. Perceptron Algorithm--zhTROHtscQ.mp4	2.0 MB
16. DL 18 S Softmax-n8S-v_LCTms.mp4	2.0 MB
25. Gradient Descent Algorithm-snxmBgi_GeU.mp4	2.1 MB
03. Classsification Example-Dh625piH7Z0.mp4	2.2 MB
21. Formula For Cross 1-qvr_ego_d6w.mp4	2.2 MB
30. Non-Linear Data-F7ZiE8PQiSc.mp4	2.2 MB
15. Discrete vs Continuous-rdP-RPDFkl0.mp4	2.4 MB
06. 09 Higher Dimensions-eBHunImDmWw.mp4	2.7 MB
09. AND And OR Perceptrons-45K5N0P9wJk.mp4	2.8 MB
32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4	3.0 MB
11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4	3.0 MB
19. Quiz - Cross 1--xxrisIvD0E.mp4	3.2 MB
32. Layers-pg99FkXYK0M.mp4	3.3 MB
28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4	3.4 MB
34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4	3.5 MB
13. Error Functions-YfUUunxWIJw.mp4	3.7 MB
10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4	3.8 MB
24. Gradient Descent-rhVIF-nigrY.mp4	3.9 MB
18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4	4.0 MB
05. Linear Boundaries-X-uMlsBi07k.mp4	4.0 MB
16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4	4.2 MB
22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4	4.3 MB
20. Cross Entropy 1-iREoPUrpXvE.mp4	4.4 MB
32. Combinando modelos-Boy3zHVrWB4.mp4	5.0 MB
23. Error Function-V5kkHldUlVU.mp4	5.1 MB
07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4	5.4 MB
33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4	5.6 MB
15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4	5.6 MB
34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4	6.0 MB
18. Maximum Likelihood 1-1yJx-QtlvNI.mp4	6.0 MB
34. Backpropagation V2-1SmY3TZTyUk.mp4	6.8 MB
21. CrossEntropy V1-1BnhC6e0TFw.mp4	6.9 MB
14. Error Functions-jfKShxGAbok.mp4	7.6 MB
02. Introduction-tn-CrUTkCUc.mp4	7.9 MB
/Part 07-Module 01-Lesson 03_Training Neural Networks/
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14. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt	1.0 KB
09. Local Minima-gF_sW_nY-xw.zh-CN.vtt	1.0 KB
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09. Local Minima-gF_sW_nY-xw.pt-BR.vtt	1.1 KB
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08. Dropout-Ty6K6YiGdBs.en.vtt	4.8 KB
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15. Momentum.html	6.2 KB
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16. Error Functions Around the World.html	6.4 KB
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04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4	6.7 MB
07. Regularization-ndYnUrx8xvs.mp4	7.9 MB
/Part 08-Module 01-Lesson 07_Deep Learning for Cancer Detection with Sebastian Thrun/
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07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt	0.5 KB
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt	0.5 KB
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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt	0.7 KB
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23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt	1.8 KB
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05. The Data-2RLbbV7MQNA.en.vtt	2.0 KB
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21. Comparing our Results with Doctors.html	8.0 KB
20. Solution ROC Curve.html	8.0 KB
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04. Medical Classification.html	8.0 KB
12. Validating the Training.html	8.0 KB
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27. Useful Resources.html	9.2 KB
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02. Skin Cancer.html	9.9 KB
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29. Mini Project Dermatologist AI.html	20.0 KB
18. Images-1GdiN5Wc8LA.mp4	404.9 KB
07. 07 Quiz Data Challenges V1-F8yc7BlV93c.mp4	883.2 KB
13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4	909.9 KB
19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4	1.2 MB
20. Solution ROC Curve-sdUUf6RRmXI.mp4	1.2 MB
16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4	1.2 MB
28. Mini Project Introduction-Rgf3YVFWl-M.mp4	1.2 MB
09. Training The Neural Network-HwiI-UXUx-M.mp4	1.3 MB
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14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4	1.7 MB
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04. Medical Classification-RCOSP60dV7U.mp4	2.3 MB
17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4	2.4 MB
22. Visualization-aGIGB4Ta3_A.mp4	2.5 MB
21. ROC Curve-fWwe_JlpnlQ.mp4	2.8 MB
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25. Confusion Matrix-3rpN-YYlfes.mp4	3.2 MB
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24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4	5.3 MB
12. Validating The Training-Oxm9ofvov3I.mp4	5.8 MB
18. ROC Curve-2Iw5TiGzJI4.mp4	7.0 MB
26. Conclusion-WhpE_8sTt-0.mp4	8.6 MB
/
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/Part 01-Module 03-Lesson 02_Intro to Neural Networks/
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04. Neural Networks.html	6.5 KB
01. Introducing Luis.html	6.5 KB
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07. OR NOT Perceptron Quiz.html	10.9 KB
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11. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt	11.1 KB
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10. Gradient Descent-29PmNG7fuuM.mp4	2.6 MB
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11. Gradient Descent-Math-7sxA5Ap8AWM.mp4	11.8 MB
04. Neural Networks-Mqogpnp1lrU.mp4	15.6 MB
/Part 07-Module 01-Lesson 05_Keras/
06. Keras Lab-a50un22BsLI.zh-CN.vtt	0.5 KB
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index.html	4.1 KB
06. Mini Project Intro.html	5.5 KB
08. Lab IMDB Data in Keras.html	6.0 KB
04. Lab Student Admissions in Keras.html	6.0 KB
01. Intro.html	6.2 KB
05. Optimizers in Keras.html	6.3 KB
07. Pre-Lab IMDB Data in Keras.html	9.8 KB
03. Pre-Lab Student Admissions in Keras.html	11.8 KB
02. Keras.html	15.9 KB
06. Keras Lab-a50un22BsLI.mp4	2.3 MB
/Part 02-Module 05-Lesson 01_Deep Neural Networks/
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03. Quiz TensorFlow ReLUs.html	9.6 KB
07. Finetuning.html	10.3 KB
04. Deep Neural Network in TensorFlow.html	11.8 KB
13. Quiz TensorFlow Dropout.html	13.9 KB
06. Save and Restore TensorFlow Models.html	15.0 KB
10. Regularization-Quiz-E0eEW6V0_sA.mp4	612.7 KB
12. Dropout Pt. 2-8nG8zzJMbZw.mp4	1.3 MB
09. Regularization-QcJBhbuCl5g.mp4	1.6 MB
05. Training a Deep Learning Network-CsB7yUtMJyk.mp4	2.7 MB
11. Dropout RENDER-6DcImJS8uV8.mp4	2.7 MB
08. Regularization Intro-pECnr-5F3_Q.mp4	8.7 MB
01. Mat HS-9P7UPWFu8w8.mp4	8.9 MB
/Part 08-Module 01-Lesson 04_Convolutional Neural Networks/
01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt	0.6 KB
01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt	0.6 KB
01. Introducing Alexis-38ExGpdyvJI.en.vtt	0.7 KB
07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt	3.2 KB
23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt	3.4 KB
04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt	3.5 KB
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt	3.6 KB
07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt	3.7 KB
12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt	3.8 KB
04. MLPs For Image Classification-TIFStebu530.en.vtt	3.9 KB
23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt	3.9 KB
07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt	3.9 KB
23. Visualizing CNNs-mnqS_EhEZVg.en.vtt	4.0 KB
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt	4.0 KB
04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt	4.2 KB
05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt	4.3 KB
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt	4.4 KB
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02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt	4.8 KB
06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt	4.9 KB
18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt	4.9 KB
05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt	4.9 KB
03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt	5.0 KB
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02. Applications of CNNs-HrYNL_1SV2Y.en.vtt	5.5 KB
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18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt	5.5 KB
15. Pooling Layers-OkkIZNs7Cyc.en.vtt	5.5 KB
06. Model Validation in Keras-002jNXSM6CU.en.vtt	5.6 KB
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02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt	5.8 KB
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06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt	6.2 KB
10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt	6.2 KB
26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt	6.3 KB
18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt	6.3 KB
25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt	6.7 KB
26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt	6.9 KB
20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt	7.2 KB
10. Convolutional Layers-h5R_JvdUrUI.en.vtt	7.4 KB
10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt	7.7 KB
12. Stride and Padding.html	7.8 KB
09. Local Connectivity.html	7.8 KB
09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt	7.8 KB
10. Convolutional Layers (Part 1).html	7.8 KB
01. Introducing Alexis.html	8.0 KB
15. Pooling Layers.html	8.0 KB
07. When do MLPs (not) work well .html	8.1 KB
20. Image Augmentation in Keras-odStujZq3GY.en.vtt	8.4 KB
04. MLPs for Image Classification.html	8.5 KB
06. Model Validation in Keras.html	8.5 KB
19. Mini Project CNNs in Keras.html	8.6 KB
11. Convolutional Layers (Part 2).html	8.6 KB
21. Mini Project Image Augmentation in Keras.html	8.7 KB
20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt	8.7 KB
11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt	8.9 KB
22. Groundbreaking CNN Architectures.html	9.0 KB
09. Local Connectivity-z9wiDg0w-Dc.en.vtt	9.2 KB
03. How Computers Interpret Images.html	9.2 KB
26. Transfer Learning in Keras.html	9.3 KB
05. Categorical Cross-Entropy.html	9.3 KB
09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt	9.5 KB
18. CNNs in Keras Practical Example.html	9.6 KB
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17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt	10.0 KB
23. Visualizing CNNs (Part 1).html	10.0 KB
11. Convolutional Layers-RnM1D-XI--8.en.vtt	10.2 KB
17. CNNs for Image Classification.html	10.9 KB
16. Max Pooling Layers in Keras.html	11.0 KB
08. Mini Project Training an MLP on MNIST.html	11.1 KB
11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt	11.3 KB
17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt	11.6 KB
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17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt	12.5 KB
02. Applications of CNNs.html	13.2 KB
24. Visualizing CNNs (Part 2).html	14.7 KB
14. Quiz Dimensionality.html	16.5 KB
25. Transfer Learning.html	19.3 KB
01. Introducing Alexis-38ExGpdyvJI.mp4	2.2 MB
04. MLPs For Image Classification-TIFStebu530.mp4	4.6 MB
06. Model Validation in Keras-002jNXSM6CU.mp4	5.5 MB
05. Categorical Cross-Entropy-3sDYifgjFck.mp4	5.7 MB
07. When do MLPs (not) work well-deMeuLdZN3Q.mp4	5.8 MB
15. Pooling Layers-OkkIZNs7Cyc.mp4	6.1 MB
03. How Computers Interpret Images-V4f6p6uRhu8.mp4	6.5 MB
12. Stride and Padding-0r9o8hprDXQ.mp4	8.4 MB
10. Convolutional Layers-h5R_JvdUrUI.mp4	8.4 MB
22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4	8.5 MB
18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4	9.1 MB
23. Visualizing CNNs-mnqS_EhEZVg.mp4	9.6 MB
20. Image Augmentation in Keras-odStujZq3GY.mp4	10.8 MB
26. Transfer Learning in Keras-HsIAznMM1LA.mp4	13.6 MB
09. Local Connectivity-z9wiDg0w-Dc.mp4	13.7 MB
25. Transfer Learning-LHG5FltaR6I.mp4	14.0 MB
02. Applications of CNNs-HrYNL_1SV2Y.mp4	18.6 MB
17. CNNs For Image Classification-l9vg_1YUlzg.mp4	19.0 MB
11. Convolutional Layers-RnM1D-XI--8.mp4	20.8 MB
/Part 02-Module 04-Lesson 02_Intro to TensorFlow/
13. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt	0.6 KB
13. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt	0.7 KB
18. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt	0.7 KB
17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt	0.7 KB
13. 13 L One Hot Encoding-phYsxqlilUk.en.vtt	0.7 KB
17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt	0.7 KB
18. Numerical Stability-_SbGcOS-jcQ.en-US.vtt	0.8 KB
21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt	0.8 KB
21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt	0.8 KB
01. Intro to Vincent-0_M6a04ofz8.zh-CN.vtt	0.8 KB
17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt	0.8 KB
18. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt	0.8 KB
01. Intro to Vincent-0_M6a04ofz8.en.vtt	0.8 KB
01. Intro to Vincent-0_M6a04ofz8.pt-BR.vtt	0.8 KB
21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt	0.8 KB
08. Supervised Classification-XTGsutypAPE.zh-CN.vtt	1.0 KB
08. Supervised Classification-XTGsutypAPE.en.vtt	1.1 KB
08. Supervised Classification-XTGsutypAPE.pt-BR.vtt	1.2 KB
04. Let'S Get Started-ySIDqaXLhHw.zh-CN.vtt	1.2 KB
04. Let'S Get Started-ySIDqaXLhHw.en.vtt	1.3 KB
04. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt	1.4 KB
02. What Is Deep Learning-INt1nULYPak.pt-BR.vtt	1.4 KB
02. What Is Deep Learning-INt1nULYPak.zh-CN.vtt	1.5 KB
02. What Is Deep Learning-INt1nULYPak.en.vtt	1.6 KB
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24. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt	2.1 KB
09. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt	2.2 KB
03. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt	2.3 KB
24. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt	2.4 KB
23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt	2.4 KB
03. Solving Problems - Big And Small-WHcRQMGSbqg.pt-BR.vtt	2.4 KB
23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt	2.5 KB
09. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt	2.5 KB
09. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt	2.5 KB
03. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt	2.5 KB
24. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt	2.6 KB
16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt	3.0 KB
16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt	3.1 KB
16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt	3.1 KB
19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt	3.6 KB
22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.zh-CN.vtt	4.0 KB
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22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.en.vtt	4.2 KB
19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.en.vtt	4.4 KB
19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.pt-BR.vtt	4.4 KB
20. 21 L Measuring Performance-byP0DJImOSk.zh-CN.vtt	5.3 KB
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index.html	6.0 KB
20. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt	6.2 KB
01. Intro to Vincent .html	7.8 KB
04. Let's Get Started .html	7.9 KB
02. What is Deep Learning .html	7.9 KB
24. Parameter Hyperspace .html	7.9 KB
08. Supervised Classification.html	7.9 KB
20. Measuring Performance .html	7.9 KB
16. Minimizing Cross Entropy.html	7.9 KB
22. Stochastic Gradient Descent.html	7.9 KB
03. Solving Problems - Big and Small .html	7.9 KB
09. Training Your Logistic Classifier .html	8.0 KB
23. Momentum and Learning Rate Decay.html	8.0 KB
21. Optimizing a Logistic Classifier.html	8.0 KB
19. Normalized Inputs and Initial Weights .html	8.0 KB
17. Practical Aspects of Learning.html	8.0 KB
18. Quiz Numerical Stability.html	8.6 KB
13. One-Hot Encoding.html	9.0 KB
07. Transition to Classification.html	9.1 KB
05. Installing TensorFlow.html	10.3 KB
06. Hello, Tensor World!.html	10.5 KB
12. Quiz TensorFlow Softmax.html	10.7 KB
14. Categorical Cross-Entropy.html	11.1 KB
15. Quiz TensorFlow Cross Entropy.html	11.1 KB
27. Lab TensorFlow Neural Network.html	12.7 KB
26. Epochs.html	15.1 KB
11. ReLU and Softmax Activation Functions.html	16.9 KB
10. Quiz TensorFlow Linear Function.html	23.7 KB
25. Quiz Mini-batch.html	27.8 KB
18. Numerical Stability-_SbGcOS-jcQ.mp4	647.1 KB
13. 13 L One Hot Encoding-phYsxqlilUk.mp4	750.0 KB
08. Supervised Classification-XTGsutypAPE.mp4	1.4 MB
04. Let'S Get Started-ySIDqaXLhHw.mp4	1.5 MB
24. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4	2.3 MB
23. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4	2.4 MB
09. Training Your Logistic Classifier-WQsdr1EJgz8.mp4	2.9 MB
01. Intro to Vincent-0_M6a04ofz8.mp4	3.2 MB
16. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4	3.2 MB
21. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4	3.6 MB
17. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4	3.8 MB
22. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4	4.1 MB
19. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4	4.3 MB
20. 21 L Measuring Performance-byP0DJImOSk.mp4	5.8 MB
02. What Is Deep Learning-INt1nULYPak.mp4	6.1 MB
03. Solving Problems - Big And Small-WHcRQMGSbqg.mp4	9.6 MB
/Part 09-Module 01-Lesson 02_Long Short-Term Memory Networks (LSTM)/
07. Remember Gate-0qlm86HaXuU.zh-CN.vtt	0.6 KB
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06. Forget Gate-iWxpfxLUPSU.zh-CN.vtt	1.1 KB
06. Forget Gate-iWxpfxLUPSU.en.vtt	1.3 KB
06. Forget Gate-iWxpfxLUPSU.pt-BR.vtt	1.4 KB
04. LSTM Architecture-ycwthhdx8ws.zh-CN.vtt	1.4 KB
04. LSTM Architecture-ycwthhdx8ws.pt-BR.vtt	1.5 KB
04. LSTM Architecture-ycwthhdx8ws.en.vtt	1.5 KB
08. LSTM 7 Use Gate-5Ifolm1jTdY.zh-CN.vtt	1.5 KB
08. LSTM 7 Use Gate-5Ifolm1jTdY.pt-BR.vtt	1.8 KB
08. LSTM 7 Use Gate-5Ifolm1jTdY.en.vtt	1.8 KB
11. Other Architectures-MsxFDuYlTuQ.zh-CN.vtt	2.1 KB
09. Putting It All Together-IF8FlKW-Zo0.zh-CN.vtt	2.2 KB
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02. RNN Vs LSTM-70MgF-IwAr8.zh-CN.vtt	4.3 KB
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03. LSTM Basics-gjb68a4XsqE.en.vtt	5.3 KB
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02. RNN vs LSTM.html	5.8 KB
03. Basics of LSTM.html	5.8 KB
04. Architecture of LSTM.html	5.8 KB
09. Putting it All Together.html	5.9 KB
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11. Other architectures.html	6.3 KB
06. The Forget Gate.html	6.3 KB
05. The Learn Gate.html	6.4 KB
08. The Use Gate.html	6.5 KB
01. Intro to LSTM.html	6.6 KB
10. Quiz.html	7.5 KB
07. Remember Gate-0qlm86HaXuU.mp4	693.2 KB
06. Forget Gate-iWxpfxLUPSU.mp4	1.1 MB
04. LSTM Architecture-ycwthhdx8ws.mp4	1.1 MB
08. LSTM 7 Use Gate-5Ifolm1jTdY.mp4	1.6 MB
09. Putting It All Together-IF8FlKW-Zo0.mp4	1.7 MB
11. Other Architectures-MsxFDuYlTuQ.mp4	1.8 MB
05. Learn Gate-aVHVI7ovbHY.mp4	2.3 MB
02. RNN Vs LSTM-70MgF-IwAr8.mp4	3.8 MB
03. LSTM Basics-gjb68a4XsqE.mp4	4.2 MB
/Part 03-Module 04-Lesson 04_Generate TV Scripts/
01. Project-3-Intro-qNpv7IjQzo0.zh-CN.vtt	0.6 KB
01. Project-3-Intro-qNpv7IjQzo0.en.vtt	0.7 KB
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01. Project-3-Intro-qNpv7IjQzo0.pt-BR.vtt	0.7 KB
index.html	3.9 KB
01. Introduction.html	5.3 KB
02. TV Script Workspace.html	5.6 KB
Project Description - Generate TV Scripts.html	6.7 KB
Project Rubric - Generate TV Scripts.html	13.2 KB
01. Project-3-Intro-qNpv7IjQzo0.mp4	683.2 KB
/Part 07-Module 01-Lesson 06_TensorFlow/
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17. Conclusion-wOiUQDgGD9E.en.vtt	0.7 KB
17. Conclusion-wOiUQDgGD9E.pt-BR.vtt	1.0 KB
index.html	4.9 KB
17. Outro.html	6.5 KB
10. Lab NotMNIST in TensorFlow.html	7.0 KB
11. Two-layer Neural Network.html	8.3 KB
01. Intro.html	8.6 KB
09. Pre-Lab NotMNIST in TensorFlow.html	8.9 KB
02. Installing TensorFlow.html	9.0 KB
03. Hello, Tensor World!.html	9.2 KB
05. Quiz TensorFlow Softmax.html	9.4 KB
06. Quiz TensorFlow Cross Entropy.html	9.8 KB
12. Quiz TensorFlow ReLUs.html	10.0 KB
15. Finetuning.html	10.7 KB
13. Deep Neural Network in TensorFlow.html	12.3 KB
08. Epochs.html	13.7 KB
16. Quiz TensorFlow Dropout.html	14.3 KB
14. Save and Restore TensorFlow Models.html	15.4 KB
04. Quiz TensorFlow Linear Function.html	22.4 KB
07. Quiz Mini-batch.html	26.5 KB
17. Conclusion-wOiUQDgGD9E.mp4	2.7 MB
/Part 01-Module 02-Lesson 01_Regression/
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01. Welcome to Week One-10M2DnJuziE.pt-BR.vtt	0.7 KB
01. Welcome to Week One-10M2DnJuziE.en-US.vtt	0.8 KB
index.html	4.1 KB
01. Welcome to Week One.html	5.5 KB
03. Siraj's Intro to Deep Learning.html	5.8 KB
07. Siraj's Live Session.html	6.2 KB
02. Preparing for Siraj's video.html	6.4 KB
05. Linear Regression Warnings.html	7.3 KB
06. Multiple Linear Regression.html	12.3 KB
03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.pt.vtt	13.8 KB
04. Linear Regression.html	16.0 KB
03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.en.vtt	16.0 KB
01. Welcome to Week One-10M2DnJuziE.mp4	4.4 MB
03. Siraj's Intro to Deep Learning - How to Make a Prediction-QN1ZwKszguE.mp4	66.5 MB
/Part 07-Module 01-Lesson 04_Sentiment Analysis/
01. Introducing Andrew Trask-ltO71Bm8b3M.zh-CN.vtt	0.7 KB
01. Introducing Andrew Trask-ltO71Bm8b3M.en.vtt	0.7 KB
01. Introducing Andrew Trask-ltO71Bm8b3M.pt-BR.vtt	1.3 KB
23. Andrew Trask - Outro-nIF0GLOQglQ.pt-BR.vtt	2.2 KB
23. Andrew Trask - Outro-nIF0GLOQglQ.zh-CN.vtt	2.5 KB
23. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt	2.8 KB
11. Building a Neural Network-aM2k7RTjjJI.pt-BR.vtt	2.9 KB
11. Building a Neural Network-aM2k7RTjjJI.zh-CN.vtt	3.0 KB
11. Building a Neural Network-aM2k7RTjjJI.en.vtt	3.4 KB
02. Andrew Trask - Intro-da1I0mea1jQ.pt-BR.vtt	3.8 KB
10. Mini Project 2 Solution-45ihpPaeO8E.pt-BR.vtt	4.0 KB
08. Transforming Text into Numbers-7rHBU5cbePE.pt-BR.vtt	4.3 KB
08. Transforming Text into Numbers-7rHBU5cbePE.zh-CN.vtt	4.4 KB
10. Mini Project 2 Solution-45ihpPaeO8E.zh-CN.vtt	4.7 KB
02. Andrew Trask - Intro-da1I0mea1jQ.zh-CN.vtt	5.1 KB
08. Transforming Text into Numbers-7rHBU5cbePE.en.vtt	5.2 KB
10. Mini Project 2 Solution-45ihpPaeO8E.en.vtt	5.2 KB
index.html	5.4 KB
02. Andrew Trask - Intro-da1I0mea1jQ.en-US.vtt	5.6 KB
23. Conclusion.html	7.2 KB
05. Framing the Problem.html	7.2 KB
19. Further Noise Reduction.html	7.2 KB
01. Introducing Andrew Trask.html	7.2 KB
11. Building a Neural Network.html	7.2 KB
14. Understanding Neural Noise.html	7.2 KB
08. Transforming Text into Numbers.html	7.2 KB
16. Understanding Inefficiencies in our Network.html	7.3 KB
13. Mini Project 3 Solution.html	7.5 KB
10. Mini Project 2 Solution.html	7.5 KB
07. Mini Project 1 Solution.html	7.5 KB
18. Mini Project 5 Solution.html	7.5 KB
02. Meet Andrew.html	7.5 KB
04. The Notebooks.html	7.6 KB
22. Analysis What's Going on in the Weights.html	7.6 KB
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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.pt-BR.vtt	8.0 KB
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07. Mini Project 1 Solution-l4r5l0HvHRI.en.vtt	9.0 KB
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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt	9.5 KB
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14. Understanding Neural Noise-ubqhh4Iv7O4.zh-CN.vtt	13.8 KB
22. Analysis What's Going on in the Weights-UHsT35pbpcE.en.vtt	14.0 KB
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14. Understanding Neural Noise-ubqhh4Iv7O4.en.vtt	16.0 KB
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13. Mini Project 3 Solution-imnxzCev4SI.en.vtt	18.9 KB
01. Introducing Andrew Trask-ltO71Bm8b3M.mp4	4.3 MB
08. Transforming Text into Numbers-7rHBU5cbePE.mp4	7.0 MB
10. Mini Project 2 Solution-45ihpPaeO8E.mp4	9.7 MB
11. Building a Neural Network-aM2k7RTjjJI.mp4	10.7 MB
23. Andrew Trask - Outro-nIF0GLOQglQ.mp4	12.4 MB
05. Framing the Problem-IsTOnkAKaJw.mp4	18.5 MB
16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.mp4	21.7 MB
19. Further Noise Reduction-Kl3hWxizKVg.mp4	23.4 MB
02. Andrew Trask - Intro-da1I0mea1jQ.mp4	24.9 MB
07. Mini Project 1 Solution-l4r5l0HvHRI.mp4	26.0 MB
18. Mini Project 5 Solution-Hv86B_jjWTI.mp4	30.3 MB
22. Analysis What's Going on in the Weights-UHsT35pbpcE.mp4	35.3 MB
21. Mini Project 6 Solution-ji0famK7gOQ.mp4	41.0 MB
14. Understanding Neural Noise-ubqhh4Iv7O4.mp4	52.7 MB
13. Mini Project 3 Solution-imnxzCev4SI.mp4	57.2 MB
/Part 11-Module 01-Lesson 02_The RL Framework The Problem/
01. Introduction-X_9l_ZqXXBA.zh-CN.vtt	0.7 KB
01. Introduction-X_9l_ZqXXBA.en.vtt	0.8 KB
01. Introduction-X_9l_ZqXXBA.pt-BR.vtt	0.9 KB
17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt	2.5 KB
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt	2.5 KB
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt	2.9 KB
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt	2.9 KB
17. MDPs, Part 3-UlXHFbla3QI.en.vtt	3.0 KB
06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt	3.1 KB
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt	3.1 KB
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt	3.3 KB
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10. Cumulative Reward-ysriH65lV9o.zh-CN.vtt	4.5 KB
02. The Setting, Revisited-V6Q1uF8a6kA.zh-CN.vtt	4.9 KB
index.html	5.0 KB
08. Goals and Rewards, Part 2-pVIFc72VYH8.zh-CN.vtt	5.1 KB
10. Cumulative Reward-ysriH65lV9o.en.vtt	5.5 KB
02. The Setting, Revisited-V6Q1uF8a6kA.en.vtt	5.7 KB
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08. Goals and Rewards, Part 2-pVIFc72VYH8.en.vtt	5.9 KB
02. The Setting, Revisited-V6Q1uF8a6kA.pt-BR.vtt	5.9 KB
11. Discounted Return-opXGNPwwn7g.zh-CN.vtt	6.1 KB
10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt	6.1 KB
08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt	6.5 KB
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10. Cumulative Reward.html	6.7 KB
06. The Reward Hypothesis.html	6.7 KB
02. The Setting, Revisited.html	6.7 KB
07. Goals and Rewards, Part 1.html	6.8 KB
03. Episodic vs. Continuing Tasks.html	6.8 KB
01. Introduction.html	6.9 KB
14. MDPs, Part 2-CUTtQvxKkNw.en.vtt	7.0 KB
11. Discounted Return-opXGNPwwn7g.en.vtt	7.3 KB
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11. Discounted Return.html	7.5 KB
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11. Discounted Return-opXGNPwwn7g.pt-BR.vtt	8.2 KB
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15. Quiz One-Step Dynamics, Part 1.html	11.1 KB
18. Finite MDPs.html	11.6 KB
12. Quiz Pole-Balancing.html	11.7 KB
09. Quiz Goals and Rewards.html	12.2 KB
19. Summary.html	12.4 KB
04. Quiz Test Your Intuition.html	12.5 KB
16. Quiz One-Step Dynamics, Part 2.html	14.8 KB
01. Introduction-X_9l_ZqXXBA.mp4	3.0 MB
13. MDPs, Part 1-NBWbluSbxPg.mp4	4.0 MB
06. The Reward Hypothesis-uAqNwgZ49JE.mp4	4.6 MB
14. MDPs, Part 2-CUTtQvxKkNw.mp4	7.2 MB
07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4	7.2 MB
02. The Setting, Revisited-V6Q1uF8a6kA.mp4	7.7 MB
08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4	8.4 MB
10. Cumulative Reward-ysriH65lV9o.mp4	10.4 MB
03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4	10.6 MB
11. Discounted Return-opXGNPwwn7g.mp4	15.0 MB
17. MDPs, Part 3-UlXHFbla3QI.mp4	15.5 MB
/Part 04-Module 02-Lesson 04_Generate Faces/
01. Last Project - Congrats-UUqU8SYBZ9Q.zh-CN.vtt	0.8 KB
01. Last Project - Congrats-UUqU8SYBZ9Q.en.vtt	0.8 KB
01. Last Project - Congrats-UUqU8SYBZ9Q.pt-BR.vtt	0.9 KB
02. P5 Intro-jvJtHYBX7sM.en.vtt	1.1 KB
02. P5 Intro-jvJtHYBX7sM.zh-CN.vtt	1.1 KB
02. P5 Intro-jvJtHYBX7sM.pt-BR.vtt	1.1 KB
index.html	3.9 KB
02. Project Introduction.html	5.2 KB
01. One Project Away!.html	5.3 KB
03. Face Generation Workspace.html	5.7 KB
Project Description - Generate Faces.html	6.1 KB
Project Rubric - Generate Faces.html	8.3 KB
02. P5 Intro-jvJtHYBX7sM.mp4	1.7 MB
01. Last Project - Congrats-UUqU8SYBZ9Q.mp4	2.3 MB
/Part 11-Module 01-Lesson 10_Policy-Based Methods/
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06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt	0.8 KB
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03. M2L3 03 V2-TePX-0Bs23E.zh-CN.vtt	5.4 KB
08. Recap.html	5.4 KB
05. Policy Gradients.html	5.4 KB
01. Policy-Based Methods.html	5.4 KB
04. Stochastic Policy Search.html	5.4 KB
02. Why Policy-Based Methods.html	5.4 KB
06. Monte Carlo Policy Gradients.html	5.4 KB
07. Constrained Policy Gradients.html	5.4 KB
03. Policy Function Approximation.html	5.4 KB
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07. M2L3 07 V2-ZBLLGIN1EfU.en.vtt	9.3 KB
06. M2L3 06 V1-RMjdQkl6CqE.mp4	3.2 MB
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08. M2L3 08 V1-og3W6CXn1F0.mp4	8.0 MB
05. M2L3 05 V1-eZxxNNIZuwA.mp4	17.5 MB
03. M2L3 03 V2-TePX-0Bs23E.mp4	19.8 MB
04. M2L3 04 V1-QicxmyE5vTo.mp4	22.0 MB
02. M2L3 02 V2-ToS8vXGdODE.mp4	34.1 MB
07. M2L3 07 V2-ZBLLGIN1EfU.mp4	45.7 MB
/Part 11-Module 01-Lesson 05_Monte Carlo Methods/
01. Introduction-W2EP3riQSus.zh-CN.vtt	0.8 KB
01. Introduction-W2EP3riQSus.en.vtt	0.9 KB
01. Introduction-W2EP3riQSus.pt-BR.vtt	1.0 KB
12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt	1.9 KB
09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt	1.9 KB
12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt	2.3 KB
09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt	2.3 KB
12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt	2.6 KB
09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt	2.6 KB
18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt	3.3 KB
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18. MC Control Constant-alpha-QFV1nI9Zpoo.pt-BR.vtt	4.1 KB
10. MC Control Incremental Mean-E2RITH-2NUE.zh-CN.vtt	4.6 KB
06. MC Prediction Action Values-08tLtbh0xLs.zh-CN.vtt	5.3 KB
index.html	5.4 KB
10. MC Control Incremental Mean-E2RITH-2NUE.en.vtt	5.6 KB
13. MC Control Policy Improvement-2RKH-BInX7s.zh-CN.vtt	5.7 KB
10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt	6.0 KB
06. MC Prediction Action Values-08tLtbh0xLs.en.vtt	6.2 KB
13. MC Control Policy Improvement-2RKH-BInX7s.en.vtt	6.9 KB
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09. Generalized Policy Iteration.html	7.2 KB
12. MC Control Policy Evaluation.html	7.3 KB
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13. MC Control Policy Improvement-2RKH-BInX7s.pt-BR.vtt	7.3 KB
03. MC Prediction State Values-0q2wSWyuBj8.zh-CN.vtt	7.4 KB
01. Introduction.html	7.4 KB
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21. Mini Project MC (Part 4).html	7.6 KB
08. Mini Project MC (Part 2).html	7.6 KB
05. Mini Project MC (Parts 0 and 1).html	7.6 KB
16. Implementation.html	8.0 KB
20. Implementation.html	8.1 KB
03. MC Prediction State Values-0q2wSWyuBj8.en.vtt	8.7 KB
07. Implementation.html	9.0 KB
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02. OpenAI Gym BlackjackEnv.html	9.6 KB
04. Implementation.html	9.7 KB
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14. Quiz Epsilon-Greedy Policies.html	14.3 KB
22. Summary.html	15.5 KB
19. MC Control Constant-alpha, Part 2.html	15.8 KB
15. Exploration vs. Exploitation.html	17.8 KB
01. Introduction-W2EP3riQSus.mp4	5.2 MB
09. Generalized Policy Iteration-XRmz4nolEsw.mp4	7.3 MB
12. MC Control Policy Evaluation-3_opwMzpEEI.mp4	9.5 MB
18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4	13.1 MB
10. MC Control Incremental Mean-E2RITH-2NUE.mp4	21.0 MB
13. MC Control Policy Improvement-2RKH-BInX7s.mp4	23.1 MB
06. MC Prediction Action Values-08tLtbh0xLs.mp4	23.1 MB
03. MC Prediction State Values-0q2wSWyuBj8.mp4	35.0 MB
/Part 11-Module 01-Lesson 04_Dynamic Programming/
01. Introduction-ek2PD9RDrWw.zh-CN.vtt	0.9 KB
01. Introduction-ek2PD9RDrWw.en.vtt	1.1 KB
01. Introduction-ek2PD9RDrWw.pt-BR.vtt	1.1 KB
04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt	1.7 KB
17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt	1.8 KB
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08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt	6.2 KB
05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt	6.4 KB
14. Policy Improvement-4_adUEK0IHg.zh-CN.vtt	7.1 KB
08. Iterative Policy Evaluation-eDXIL_oOJHI.en.vtt	7.5 KB
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08. Iterative Policy Evaluation.html	7.6 KB
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16. Mini Project DP (Part 3).html	8.0 KB
25. Mini Project DP (Part 6).html	8.0 KB
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10. Mini Project DP (Parts 0 and 1).html	8.0 KB
04. Another Gridworld Example.html	8.1 KB
01. Introduction.html	8.2 KB
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18. Implementation.html	8.5 KB
14. Policy Improvement-4_adUEK0IHg.pt-BR.vtt	9.2 KB
12. Implementation.html	9.3 KB
26. Check Your Understanding.html	9.4 KB
15. Implementation.html	10.0 KB
21. Implementation.html	10.1 KB
03. Your Workspace.html	10.2 KB
24. Implementation.html	10.4 KB
02. OpenAI Gym FrozenLakeEnv.html	10.7 KB
07. Quiz An Iterative Method.html	11.8 KB
11. Action Values.html	12.4 KB
09. Implementation.html	13.1 KB
06. An Iterative Method, Part 2.html	13.6 KB
27. Summary.html	14.2 KB
04. Another Gridworld Example-n9SbomnLb-U.mp4	4.9 MB
01. Introduction-ek2PD9RDrWw.mp4	6.5 MB
17. Policy Iteration-gqv7o1kBDc0.mp4	8.5 MB
20. Truncated Policy Iteration-a-RvCxlPMho.mp4	14.8 MB
23. Value Iteration-XNeQn8N36y8.mp4	16.4 MB
08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4	27.9 MB
05. An Iterative Method-AX-hG3KvwzY.mp4	28.9 MB
14. Policy Improvement-4_adUEK0IHg.mp4	31.9 MB
/Part 11-Module 01-Lesson 11_Actor-Critic Methods/
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06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.en.vtt	1.0 KB
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt	1.2 KB
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt	1.4 KB
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt	1.5 KB
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt	1.6 KB
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt	1.7 KB
01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt	1.8 KB
03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt	2.0 KB
07. Summary-hvYQ_3LgCYs.zh-CN.vtt	2.1 KB
07. Summary-hvYQ_3LgCYs.en.vtt	2.4 KB
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt	2.4 KB
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt	2.5 KB
02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt	2.7 KB
04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt	2.8 KB
07. Summary-hvYQ_3LgCYs.pt-BR.vtt	2.9 KB
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02. A Better Score Function.html	5.6 KB
04. The Actor and The Critic.html	5.6 KB
05. Advantage Function.html	5.6 KB
03. Two Function Approximators.html	5.6 KB
01. Actor-Critic Methods.html	5.6 KB
06. Actor-Critic with Advantage.html	5.6 KB
06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4	3.2 MB
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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4	10.5 MB
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05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4	11.2 MB
07. Summary-hvYQ_3LgCYs.mp4	17.7 MB
/img/
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/Part 01-Module 01-Lesson 01_Welcome/
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08. We Value Your Feedback-Dl23R0YCQ0U.pt-BR.vtt	1.0 KB
08. We Value Your Feedback-Dl23R0YCQ0U.en-US.vtt	1.0 KB
04. The first week-krK-TcGoYUI.zh-CN.vtt	1.1 KB
04. The first week-krK-TcGoYUI.pt-BR.vtt	1.2 KB
04. The first week-krK-TcGoYUI.en-US.vtt	1.2 KB
09. Getting-Setup-1SuxTnuQkeE.zh-CN.vtt	1.3 KB
09. Getting-Setup-1SuxTnuQkeE.pt-BR.vtt	1.3 KB
09. Getting-Setup-1SuxTnuQkeE.en.vtt	1.4 KB
01. Welcome-PdPdogFHnvE.zh-CN.vtt	1.9 KB
03. Meet Your Instructors -EcP0U4720sA.pt-BR.vtt	2.0 KB
03. Meet Your Instructors -EcP0U4720sA.zh-CN.vtt	2.0 KB
03. Meet Your Instructors -EcP0U4720sA.en.vtt	2.0 KB
01. Welcome-PdPdogFHnvE.pt-BR.vtt	2.0 KB
03. Meet Your Instructors -EcP0U4720sA.en-US.vtt	2.1 KB
01. Welcome-PdPdogFHnvE.en.vtt	2.4 KB
02. Projects You Will Build-yDPuDuCMST8.en.vtt	2.7 KB
index.html	4.2 KB
01. Welcome to the Deep Learning Nanodegree Foundations.html	5.7 KB
08. We Value Your Feedback.html	6.0 KB
09. Getting Set Up.html	6.0 KB
05. Prerequisites.html	6.2 KB
03. Meet Your Instructors.html	6.2 KB
04. The First Week.html	7.1 KB
02. Projects You Will Build .html	7.5 KB
06. Community Support.html	12.5 KB
07. Deadline Policy.html	16.6 KB
09. Getting-Setup-1SuxTnuQkeE.mp4	6.9 MB
08. We Value Your Feedback-Dl23R0YCQ0U.mp4	7.2 MB
04. The first week-krK-TcGoYUI.mp4	7.4 MB
02. Projects You Will Build-yDPuDuCMST8.mp4	9.3 MB
03. Meet Your Instructors -EcP0U4720sA.mp4	14.0 MB
01. Welcome-PdPdogFHnvE.mp4	18.4 MB
/Part 04-Module 01-Lesson 01_Generative Adversarial Networks/
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09. Discriminator Network-nWXxT8OqCfs.zh-CN.vtt	1.1 KB
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01. GANs Intro-F7XgI6TmaGI.pt-BR.vtt	1.3 KB
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14. Training Optimizers-AU5gH7LS57E.zh-CN.vtt	2.7 KB
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12. Building the Network Solution-Ikp3rVzG970.pt-BR.vtt	3.0 KB
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12. Building the Network Solution-Ikp3rVzG970.zh-CN.vtt	3.2 KB
13. Training Losses-IaAeDrXMEcU.zh-CN.vtt	3.4 KB
11. Building the Network-5sZkRSHfiAE.pt-BR.vtt	3.4 KB
12. Building the Network Solution-Ikp3rVzG970.en.vtt	3.5 KB
11. Building the Network-5sZkRSHfiAE.zh-CN.vtt	3.5 KB
13. Training Losses-IaAeDrXMEcU.pt-BR.vtt	3.7 KB
07. Getting Started with GANs-QA2ntKUha4g.zh-CN.vtt	3.7 KB
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15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.zh-CN.vtt	4.7 KB
index.html	5.0 KB
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16. A Trained GAN.html	6.6 KB
13. Training Losses.html	6.6 KB
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11. Building the Network.html	6.6 KB
09. Discriminator Network.html	6.6 KB
07. Get started with a GAN.html	6.7 KB
05. Practical tips and tricks for training GANs.html	6.7 KB
02. What can you do with GANs.html	6.7 KB
12. Building the Network Solution.html	6.7 KB
01. Introducing Ian Goodfellow.html	6.7 KB
04. Games and Equilibria.html	6.7 KB
10. Generator and Discriminator Solutions.html	6.7 KB
15. Training Losses and Optimizers Solution.html	6.8 KB
08. Generator Network-btHVXnICmzQ.en.vtt	6.9 KB
03. How GANs work.html	7.0 KB
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06. Build a GAN.html	7.4 KB
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04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.zh-CN.vtt	9.7 KB
16. A Trained GAN-TR-uEJcjig4.en.vtt	10.2 KB
05. GANs Architecture -gaEs7ccZv_Q.zh-CN.vtt	10.5 KB
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05. GANs Architecture -gaEs7ccZv_Q.en.vtt	12.0 KB
05. GANs Architecture -gaEs7ccZv_Q.pt-BR.vtt	12.8 KB
09. Discriminator Network-nWXxT8OqCfs.mp4	1.1 MB
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16. A Trained GAN-TR-uEJcjig4.mp4	12.2 MB
02. Cool Things To Do With GANs-bo-ToTdhgew.mp4	12.6 MB
04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.mp4	14.1 MB
05. GANs Architecture -gaEs7ccZv_Q.mp4	16.7 MB
/Part 10-Module 01-Lesson 01_Generative Adversarial Networks/
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09. Discriminator Network-nWXxT8OqCfs.zh-CN.vtt	1.1 KB
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14. Training Optimizers-AU5gH7LS57E.zh-CN.vtt	2.7 KB
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12. Building the Network Solution-Ikp3rVzG970.pt-BR.vtt	3.0 KB
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12. Building the Network Solution-Ikp3rVzG970.zh-CN.vtt	3.2 KB
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11. Building the Network-5sZkRSHfiAE.pt-BR.vtt	3.4 KB
12. Building the Network Solution-Ikp3rVzG970.en.vtt	3.5 KB
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13. Training Losses-IaAeDrXMEcU.pt-BR.vtt	3.7 KB
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15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.zh-CN.vtt	4.7 KB
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15. Training Losses and Optimizers Solutions-HlABZ9Q7xEo.pt-BR.vtt	5.1 KB
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13. Training Losses.html	6.6 KB
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14. Training Optimizers.html	6.6 KB
11. Building the Network.html	6.6 KB
09. Discriminator Network.html	6.6 KB
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05. Practical tips and tricks for training GANs.html	6.7 KB
02. What can you do with GANs.html	6.7 KB
12. Building the Network Solution.html	6.7 KB
01. Introducing Ian Goodfellow.html	6.7 KB
04. Games and Equilibria.html	6.7 KB
10. Generator and Discriminator Solutions.html	6.7 KB
15. Training Losses and Optimizers Solution.html	6.8 KB
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06. Build a GAN.html	7.4 KB
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05. GANs Architecture -gaEs7ccZv_Q.en.vtt	12.0 KB
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16. A Trained GAN-TR-uEJcjig4.mp4	12.2 MB
02. Cool Things To Do With GANs-bo-ToTdhgew.mp4	12.6 MB
04. Games, Equilibrium, GANs Solution Render-2zi8DOWIVas.mp4	14.1 MB
05. GANs Architecture -gaEs7ccZv_Q.mp4	16.7 MB
/Part 03-Module 07-Lesson 03_Translation Project/
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01. Machine Translation Intro-5thBwpcYoiI.mp4	4.0 MB
/Part 11-Module 01-Lesson 06_Temporal-Difference Methods/
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04. Implementation.html	8.3 KB
02. OpenAI Gym CliffWalkingEnv.html	8.5 KB
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08. Implementation.html	8.8 KB
03. TD Prediction TD(0)-CsD6b0csU7o.en.vtt	9.0 KB
17. Summary.html	10.6 KB
13. TD Control Expected Sarsa-kEKupCyU0P0.mp4	4.5 MB
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01. Introduction-yXErXQulI_o.mp4	21.7 MB
03. TD Prediction TD(0)-CsD6b0csU7o.mp4	31.6 MB
/Part 11-Module 01-Lesson 09_Deep Q-Learning/
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01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt	1.3 KB
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02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt	3.3 KB
04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt	3.7 KB
02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt	3.8 KB
09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.zh-CN.vtt	3.9 KB
02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt	4.0 KB
04. Temporal Difference Learning-lpmDi0QeUm8.en.vtt	4.2 KB
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04. Temporal Difference Learning-lpmDi0QeUm8.pt-BR.vtt	4.6 KB
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08. Fixed Q Targets-SWpyiEezfp4.pt-BR.vtt	6.1 KB
01. Intro to Deep Q-Learning.html	6.1 KB
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02. Neural Nets as Value Functions.html	6.1 KB
08. Fixed Q Targets.html	6.3 KB
05. Q-Learning.html	6.3 KB
03. Monte Carlo Learning.html	6.3 KB
06. Deep Q Network.html	6.4 KB
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06. Deep Q Network-GgtR_d1OB-M.pt-BR.vtt	6.5 KB
12. TensorFlow Implementation.html	6.5 KB
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07. Experience Replay-wX_-SZG-YMQ.zh-CN.vtt	8.4 KB
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13. Wrap Up-x6JggcDTcys.mp4	7.6 MB
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09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4	18.3 MB
08. Fixed Q Targets-SWpyiEezfp4.mp4	22.0 MB
06. Deep Q Network-GgtR_d1OB-M.mp4	26.9 MB
10. DQN Improvements-Zfdbp93A2GU.mp4	41.3 MB
07. Experience Replay-wX_-SZG-YMQ.mp4	50.7 MB
/img/
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/Part 11-Module 01-Lesson 03_The RL Framework The Solution/
01. Introduction-9Wyf5Zsska8.zh-CN.vtt	1.2 KB
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01. Introduction-9Wyf5Zsska8.pt-BR.vtt	1.4 KB
04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt	2.1 KB
04. Gridworld Example-XeHBmPFqTsE.en.vtt	2.6 KB
04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt	2.9 KB
06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt	3.1 KB
11. Optimal Policies-2rguYpVyCto.zh-CN.vtt	3.4 KB
06. Bellman Equations-UgIaDMvSdUo.en.vtt	3.9 KB
08. Optimality-j231aRV74QM.zh-CN.vtt	3.9 KB
06. Bellman Equations-UgIaDMvSdUo.pt-BR.vtt	4.0 KB
05. State-Value Functions-llakAjwox_8.zh-CN.vtt	4.0 KB
11. Optimal Policies-2rguYpVyCto.en.vtt	4.0 KB
09. Action-Value Functions-KJLaRfOOPGA.zh-CN.vtt	4.0 KB
02. Policies-hc3LrvaC13U.zh-CN.vtt	4.1 KB
11. Optimal Policies-2rguYpVyCto.pt-BR.vtt	4.3 KB
index.html	4.5 KB
08. Optimality-j231aRV74QM.en.vtt	4.7 KB
09. Action-Value Functions-KJLaRfOOPGA.en.vtt	4.8 KB
05. State-Value Functions-llakAjwox_8.en.vtt	4.8 KB
02. Policies-hc3LrvaC13U.en.vtt	4.9 KB
08. Optimality-j231aRV74QM.pt-BR.vtt	5.2 KB
05. State-Value Functions-llakAjwox_8.pt-BR.vtt	5.4 KB
09. Action-Value Functions-KJLaRfOOPGA.pt-BR.vtt	5.4 KB
02. Policies-hc3LrvaC13U.pt-BR.vtt	5.6 KB
02. Policies.html	6.0 KB
08. Optimality.html	6.0 KB
11. Optimal Policies.html	6.0 KB
04. Gridworld Example.html	6.0 KB
01. Introduction.html	6.2 KB
09. Action-Value Functions.html	6.9 KB
10. Quiz Action-Value Functions.html	7.0 KB
05. State-Value Functions.html	7.4 KB
06. Bellman Equations.html	10.7 KB
13. Summary.html	11.3 KB
03. Quiz Interpret the Policy.html	12.0 KB
07. Quiz State-Value Functions.html	12.6 KB
12. Quiz Optimal Policies.html	12.8 KB
04. Gridworld Example-XeHBmPFqTsE.mp4	2.5 MB
06. Bellman Equations-UgIaDMvSdUo.mp4	4.3 MB
05. State-Value Functions-llakAjwox_8.mp4	5.5 MB
01. Introduction-9Wyf5Zsska8.mp4	5.7 MB
08. Optimality-j231aRV74QM.mp4	6.3 MB
09. Action-Value Functions-KJLaRfOOPGA.mp4	6.9 MB
11. Optimal Policies-2rguYpVyCto.mp4	7.5 MB
02. Policies-hc3LrvaC13U.mp4	21.2 MB
/Part 02-Module 02-Lesson 01_Sentiment Analysis with Andrew Trask/
01. Introducing Andrew Trask-U3PqQF-8qyI.zh-CN.vtt	1.2 KB
01. Introducing Andrew Trask-U3PqQF-8qyI.en.vtt	1.3 KB
01. Introducing Andrew Trask-U3PqQF-8qyI.pt-BR.vtt	1.5 KB
22. Andrew Trask - Outro-nIF0GLOQglQ.pt-BR.vtt	2.2 KB
22. Andrew Trask - Outro-nIF0GLOQglQ.zh-CN.vtt	2.5 KB
22. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt	2.8 KB
10. Building a Neural Network-aM2k7RTjjJI.pt-BR.vtt	2.9 KB
10. Building a Neural Network-aM2k7RTjjJI.zh-CN.vtt	3.0 KB
10. Building a Neural Network-aM2k7RTjjJI.en.vtt	3.4 KB
02. Andrew Trask - Intro-da1I0mea1jQ.pt-BR.vtt	3.8 KB
09. Mini Project 2 Solution-45ihpPaeO8E.pt-BR.vtt	4.0 KB
07. Transforming Text into Numbers-7rHBU5cbePE.pt-BR.vtt	4.3 KB
07. Transforming Text into Numbers-7rHBU5cbePE.zh-CN.vtt	4.4 KB
09. Mini Project 2 Solution-45ihpPaeO8E.zh-CN.vtt	4.7 KB
02. Andrew Trask - Intro-da1I0mea1jQ.zh-CN.vtt	5.1 KB
07. Transforming Text into Numbers-7rHBU5cbePE.en.vtt	5.2 KB
09. Mini Project 2 Solution-45ihpPaeO8E.en.vtt	5.2 KB
index.html	5.4 KB
02. Andrew Trask - Intro-da1I0mea1jQ.en-US.vtt	5.6 KB
22. Conclusion.html	7.1 KB
04. Framing the Problem.html	7.1 KB
18. Further Noise Reduction.html	7.1 KB
10. Building a Neural Network.html	7.1 KB
13. Understanding Neural Noise.html	7.1 KB
07. Transforming Text into Numbers.html	7.2 KB
15. Understanding Inefficiencies in our Network.html	7.3 KB
06. Mini Project 1 Solution.html	7.4 KB
09. Mini Project 2 Solution.html	7.4 KB
12. Mini Project 3 Solution.html	7.4 KB
17. Mini Project 5 Solution.html	7.4 KB
02. Meet Andrew.html	7.4 KB
01. Introducing Andrew Trask.html	7.4 KB
21. Analysis What's Going on in the Weights.html	7.5 KB
20. Mini Project 6 Solution.html	7.5 KB
05. Mini Project 1.html	7.7 KB
14. Mini Project 4.html	7.7 KB
06. Mini Project 1 Solution-l4r5l0HvHRI.pt-BR.vtt	7.8 KB
15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.pt-BR.vtt	8.0 KB
04. Framing the Problem-IsTOnkAKaJw.pt-BR.vtt	8.0 KB
17. Mini Project 5 Solution-Hv86B_jjWTI.zh-CN.vtt	8.4 KB
15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.zh-CN.vtt	8.4 KB
17. Mini Project 5 Solution-Hv86B_jjWTI.pt-BR.vtt	8.5 KB
04. Framing the Problem-IsTOnkAKaJw.zh-CN.vtt	8.5 KB
16. Mini Project 5.html	8.6 KB
11. Mini Project 3.html	8.7 KB
19. Mini Project 6.html	8.7 KB
04. Framing the Problem-IsTOnkAKaJw.en.vtt	9.0 KB
06. Mini Project 1 Solution-l4r5l0HvHRI.en.vtt	9.0 KB
06. Mini Project 1 Solution-l4r5l0HvHRI.zh-CN.vtt	9.1 KB
03. Materials.html	9.1 KB
15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt	9.5 KB
08. Mini Project 2.html	9.6 KB
17. Mini Project 5 Solution-Hv86B_jjWTI.en.vtt	9.8 KB
18. Further Noise Reduction-Kl3hWxizKVg.zh-CN.vtt	10.2 KB
18. Further Noise Reduction-Kl3hWxizKVg.pt-BR.vtt	10.4 KB
18. Further Noise Reduction-Kl3hWxizKVg.en.vtt	11.2 KB
21. Analysis What's Going on in the Weights-UHsT35pbpcE.zh-CN.vtt	12.4 KB
20. Mini Project 6 Solution-ji0famK7gOQ.zh-CN.vtt	12.4 KB
20. Mini Project 6 Solution-ji0famK7gOQ.pt-BR.vtt	12.5 KB
21. Analysis What's Going on in the Weights-UHsT35pbpcE.pt-BR.vtt	12.9 KB
13. Understanding Neural Noise-ubqhh4Iv7O4.zh-CN.vtt	13.8 KB
21. Analysis What's Going on in the Weights-UHsT35pbpcE.en.vtt	14.0 KB
13. Understanding Neural Noise-ubqhh4Iv7O4.pt-BR.vtt	14.0 KB
20. Mini Project 6 Solution-ji0famK7gOQ.en.vtt	14.3 KB
13. Understanding Neural Noise-ubqhh4Iv7O4.en.vtt	16.0 KB
12. Mini Project 3 Solution-imnxzCev4SI.pt-BR.vtt	16.2 KB
12. Mini Project 3 Solution-imnxzCev4SI.zh-CN.vtt	16.2 KB
12. Mini Project 3 Solution-imnxzCev4SI.en.vtt	18.9 KB
07. Transforming Text into Numbers-7rHBU5cbePE.mp4	7.0 MB
09. Mini Project 2 Solution-45ihpPaeO8E.mp4	9.7 MB
10. Building a Neural Network-aM2k7RTjjJI.mp4	10.7 MB
01. Introducing Andrew Trask-U3PqQF-8qyI.mp4	11.3 MB
22. Andrew Trask - Outro-nIF0GLOQglQ.mp4	12.4 MB
04. Framing the Problem-IsTOnkAKaJw.mp4	18.5 MB
15. Understanding Inefficiencies in our Network-4MuS-6ATxCU.mp4	21.7 MB
18. Further Noise Reduction-Kl3hWxizKVg.mp4	23.4 MB
02. Andrew Trask - Intro-da1I0mea1jQ.mp4	24.9 MB
06. Mini Project 1 Solution-l4r5l0HvHRI.mp4	26.0 MB
17. Mini Project 5 Solution-Hv86B_jjWTI.mp4	30.3 MB
21. Analysis What's Going on in the Weights-UHsT35pbpcE.mp4	35.3 MB
20. Mini Project 6 Solution-ji0famK7gOQ.mp4	41.0 MB
13. Understanding Neural Noise-ubqhh4Iv7O4.mp4	52.7 MB
12. Mini Project 3 Solution-imnxzCev4SI.mp4	57.2 MB
/img/
linear-equation.gif	1.3 KB
mnist-012.png	20.7 KB
weights-0-1-2.png	25.2 KB
session.png	31.6 KB
relu-network.png	31.8 KB
softmax-input-output.png	53.7 KB
notmnist.png	55.5 KB
cross-entropy-diagram.png	64.2 KB
dropout-node.jpeg	64.2 KB
tensorflow.png	87.3 KB
meme.png	214.1 KB
multi-layer.png	219.5 KB
layers.png	293.0 KB
/img/
linear-equation.gif	1.3 KB
softmax-math.png	4.4 KB
z93yz2vrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaecamayaacbngamaajamjaeaaegtxgaaakqjywaaan	6.6 KB
mnist-012.png	20.7 KB
weights-0-1-2.png	25.2 KB
softmax.png	27.7 KB
06-l-supervised-classification-391-1.jpg	28.3 KB
session.png	31.6 KB
relu.png	33.9 KB
softmax-input-output.png	53.7 KB
notmnist.png	55.5 KB
sigmoids.png	59.6 KB
cross-entropy-diagram.png	64.2 KB
tensorflow.png	87.3 KB
download-repo.png	375.4 KB
/Part 06-Module 01-Lesson 01_Welcome to Deep Learning/
10. Getting-Setup-1SuxTnuQkeE.zh-CN.vtt	1.3 KB
10. Getting-Setup-1SuxTnuQkeE.pt-BR.vtt	1.3 KB
10. Getting-Setup-1SuxTnuQkeE.en.vtt	1.4 KB
02. Meet Your Instructors--UOFRxCu414.zh-CN.vtt	1.7 KB
02. Meet Your Instructors--UOFRxCu414.en.vtt	1.8 KB
02. Meet Your Instructors--UOFRxCu414.pt-BR.vtt	2.0 KB
05. Projects You will Build-PqpdX7YxTlU.zh-CN.vtt	2.3 KB
05. Projects You will Build-PqpdX7YxTlU.en.vtt	2.5 KB
05. Projects You will Build-PqpdX7YxTlU.pt-BR.vtt	2.9 KB
index.html	4.3 KB
01. Welcome to the Deep Learning Nanodegree Program.html	5.5 KB
02. Meet Your Instructors.html	5.8 KB
10. Getting Set Up.html	6.1 KB
05. Projects You Will Build.html	6.2 KB
09. Prerequisites.html	6.2 KB
08. Community Guidelines.html	8.1 KB
03. Learning Plan.html	8.3 KB
07. Udacity Support.html	9.7 KB
04. Program Structure.html	13.1 KB
06. Deadline Policy.html	16.7 KB
02. Meet Your Instructors--UOFRxCu414.mp4	6.1 MB
05. Projects You will Build-PqpdX7YxTlU.mp4	6.5 MB
10. Getting-Setup-1SuxTnuQkeE.mp4	6.9 MB
01. 01 Welcome To The Deep Learning Program-3QPEmwq2NaE.mp4	15.0 MB
/Part 11-Module 01-Lesson 08_RL in Continuous Spaces/
13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt	1.3 KB
14. Summary-MTEBk43oByU.zh-CN.vtt	1.4 KB
13. Non-Linear Function Approximation-rITnmpD2mN8.en.vtt	1.6 KB
14. Summary-MTEBk43oByU.en.vtt	1.7 KB
13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt	1.8 KB
14. Summary-MTEBk43oByU.pt-BR.vtt	1.9 KB
12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt	2.6 KB
09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt	2.8 KB
12. Kernel Functions-RdkPVYyVOvU.en.vtt	3.0 KB
09. Coarse Coding-Uu1J5KLAfTU.en.vtt	3.1 KB
12. Kernel Functions-RdkPVYyVOvU.pt-BR.vtt	3.2 KB
07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt	3.4 KB
09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt	3.5 KB
10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt	3.7 KB
07. Tile Coding-BRs7AnTZ_8k.en.vtt	3.9 KB
05. Discretization-j2eZyUpy--E.zh-CN.vtt	4.2 KB
10. Function Approximation-UTGWVY6jEdg.en.vtt	4.2 KB
07. Tile Coding-BRs7AnTZ_8k.pt-BR.vtt	4.3 KB
index.html	4.6 KB
05. Discretization-j2eZyUpy--E.en.vtt	4.7 KB
10. Function Approximation-UTGWVY6jEdg.pt-BR.vtt	4.7 KB
05. Discretization-j2eZyUpy--E.pt-BR.vtt	5.3 KB
01. Deep Reinforcement Learning-GPjK124RU5g.zh-CN.vtt	5.8 KB
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.zh-CN.vtt	5.9 KB
11. Linear Function Approximation-OJ5wrB7o-pI.zh-CN.vtt	5.9 KB
14. Summary.html	6.1 KB
07. Tile Coding.html	6.1 KB
09. Coarse Coding.html	6.1 KB
05. Discretization.html	6.2 KB
12. Kernel Functions.html	6.2 KB
11. Linear Function Approximation.html	6.3 KB
03. Discrete vs. Continuous Spaces.html	6.3 KB
13. Non-Linear Function Approximation.html	6.3 KB
08. Exercise Tile Coding.html	6.5 KB
06. Exercise Discretization.html	6.5 KB
01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt	6.6 KB
01. Deep Reinforcement Learning.html	6.7 KB
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt	6.7 KB
11. Linear Function Approximation-OJ5wrB7o-pI.en.vtt	7.1 KB
04. Quiz Space Representations.html	7.2 KB
01. Deep Reinforcement Learning-GPjK124RU5g.pt-BR.vtt	7.5 KB
10. Function Approximation.html	7.5 KB
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.pt-BR.vtt	7.8 KB
11. Linear Function Approximation-OJ5wrB7o-pI.pt-BR.vtt	8.0 KB
02. Resources.html	8.4 KB
13. Non-Linear Function Approximation-rITnmpD2mN8.mp4	5.2 MB
12. Kernel Functions-RdkPVYyVOvU.mp4	9.3 MB
14. Summary-MTEBk43oByU.mp4	10.4 MB
09. Coarse Coding-Uu1J5KLAfTU.mp4	10.8 MB
07. Tile Coding-BRs7AnTZ_8k.mp4	11.6 MB
05. Discretization-j2eZyUpy--E.mp4	13.2 MB
10. Function Approximation-UTGWVY6jEdg.mp4	21.1 MB
03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4	22.4 MB
11. Linear Function Approximation-OJ5wrB7o-pI.mp4	30.1 MB
01. Deep Reinforcement Learning-GPjK124RU5g.mp4	34.8 MB
/Part 09-Module 01-Lesson 01_Recurrent Neural Networks/
01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.zh-CN.vtt	1.4 KB
09. Regra da cadeia-YAhIBOnbt54.zh-CN.vtt	1.5 KB
01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.en.vtt	1.5 KB
01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.pt-BR.vtt	1.7 KB
09. Regra da cadeia-YAhIBOnbt54.en.vtt	1.7 KB
09. Regra da cadeia-YAhIBOnbt54.pt-BR.vtt	1.8 KB
08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.zh-CN.vtt	2.2 KB
14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.zh-CN.vtt	2.3 KB
08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.en.vtt	2.6 KB
14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.en.vtt	2.6 KB
04. 03 RNN Applications V3 Final-6JbTNARuKII.zh-CN.vtt	2.6 KB
08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.pt-BR.vtt	2.8 KB
04. 03 RNN Applications V3 Final-6JbTNARuKII.en.vtt	2.8 KB
14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.pt-BR.vtt	2.8 KB
19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.zh-CN.vtt	2.9 KB
04. 03 RNN Applications V3 Final-6JbTNARuKII.pt-BR.vtt	3.1 KB
19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.en.vtt	3.4 KB
03. 02 RNN History V4 Final-HbxAnYUfRnc.zh-CN.vtt	3.4 KB
17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.zh-CN.vtt	3.6 KB
19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.pt-BR.vtt	3.8 KB
03. 02 RNN History V4 Final-HbxAnYUfRnc.en.vtt	3.8 KB
18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.zh-CN.vtt	3.8 KB
09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.zh-CN.vtt	3.8 KB
17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.en.vtt	4.1 KB
05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.zh-CN.vtt	4.1 KB
02. 01 RNN Intro V6 Final-AIQEqg6F38A.zh-CN.vtt	4.2 KB
03. 02 RNN History V4 Final-HbxAnYUfRnc.pt-BR.vtt	4.2 KB
17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.pt-BR.vtt	4.4 KB
09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.en.vtt	4.5 KB
18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.en.vtt	4.5 KB
02. 01 RNN Intro V6 Final-AIQEqg6F38A.en.vtt	4.6 KB
06. 06 FeedForward A V7 Final-4rCfnWbx8-0.zh-CN.vtt	4.6 KB
05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.en.vtt	4.6 KB
09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.pt-BR.vtt	4.7 KB
13. 16 RNN B V4 Final-wsif3p5t7CI.zh-CN.vtt	4.7 KB
16. 18 RNN Example V5 Final-MDLk3fhpTx0.zh-CN.vtt	4.7 KB
05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.pt-BR.vtt	4.8 KB
18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.pt-BR.vtt	4.8 KB
02. 01 RNN Intro V6 Final-AIQEqg6F38A.pt-BR.vtt	4.9 KB
24. RNN Summary-nXP0oGGRrO8.zh-CN.vtt	5.0 KB
05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.zh-CN.vtt	5.1 KB
16. 18 RNN Example V5 Final-MDLk3fhpTx0.en.vtt	5.2 KB
12. 14 RNN A V4 Final-ofbnDxGSUcg.zh-CN.vtt	5.2 KB
06. 06 FeedForward A V7 Final-4rCfnWbx8-0.en.vtt	5.3 KB
13. 16 RNN B V4 Final-wsif3p5t7CI.en.vtt	5.4 KB
25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.zh-CN.vtt	5.4 KB
16. 18 RNN Example V5 Final-MDLk3fhpTx0.pt-BR.vtt	5.6 KB
index.html	5.6 KB
24. RNN Summary-nXP0oGGRrO8.en.vtt	5.7 KB
05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.en.vtt	5.8 KB
12. 14 RNN A V4 Final-ofbnDxGSUcg.en.vtt	5.8 KB
06. 06 FeedForward A V7 Final-4rCfnWbx8-0.pt-BR.vtt	5.8 KB
25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.en.vtt	5.9 KB
12. 14 RNN A V4 Final-ofbnDxGSUcg.pt-BR.vtt	5.9 KB
13. 16 RNN B V4 Final-wsif3p5t7CI.pt-BR.vtt	6.1 KB
05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.pt-BR.vtt	6.2 KB
24. RNN Summary-nXP0oGGRrO8.pt-BR.vtt	6.3 KB
25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.pt-BR.vtt	6.8 KB
06. 07 FeedForward B V3-kTYbTVh1d0k.zh-CN.vtt	6.9 KB
10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.zh-CN.vtt	7.0 KB
08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.zh-CN.vtt	7.4 KB
01. Introducing Ortal .html	7.6 KB
14. RNN- Unfolded Model.html	7.7 KB
26. Wrap Up.html	7.7 KB
06. 07 FeedForward B V3-kTYbTVh1d0k.en.vtt	7.7 KB
16. RNN- Example.html	8.0 KB
06. 07 FeedForward B V3-kTYbTVh1d0k.pt-BR.vtt	8.0 KB
02. RNN Introduction.html	8.2 KB
10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.en.vtt	8.5 KB
08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.pt-BR.vtt	8.6 KB
08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.en.vtt	8.7 KB
21. BPTT Quiz 2.html	8.8 KB
15. Unfolded Model Quiz.html	8.9 KB
04. RNN Applications.html	9.1 KB
20. BPTT Quiz 1.html	9.2 KB
10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.pt-BR.vtt	9.2 KB
12. RNN (part a).html	9.7 KB
25. From RNN to LSTM.html	10.1 KB
07. Feedforward Quiz.html	10.5 KB
03. RNN History.html	10.9 KB
05. Feedforward Neural Network-Reminder.html	11.3 KB
11. Backpropagation Quiz.html	11.6 KB
08. Backpropagation- Theory.html	12.1 KB
22. BPTT Quiz 3.html	12.4 KB
13. RNN (part b).html	12.8 KB
17. Backpropagation Through Time (part a).html	13.1 KB
24. RNN Summary.html	13.9 KB
23. Some more math.html	13.9 KB
19. Backpropagation Through Time (part c).html	14.4 KB
09. Backpropagation - Example (part a).html	15.1 KB
06. The Feedforward Process.html	15.1 KB
18. Backpropagation Through Time (part b).html	18.0 KB
10. Backpropagation- Example (part b).html	19.3 KB
09. Regra da cadeia-YAhIBOnbt54.mp4	1.5 MB
24. RNN Summary-nXP0oGGRrO8.mp4	7.6 MB
12. 14 RNN A V4 Final-ofbnDxGSUcg.mp4	10.9 MB
14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.mp4	11.0 MB
01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.mp4	11.2 MB
08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.mp4	13.4 MB
09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.mp4	15.5 MB
19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.mp4	18.1 MB
04. 03 RNN Applications V3 Final-6JbTNARuKII.mp4	18.1 MB
06. 06 FeedForward A V7 Final-4rCfnWbx8-0.mp4	18.6 MB
18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.mp4	19.0 MB
05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.mp4	20.6 MB
02. 01 RNN Intro V6 Final-AIQEqg6F38A.mp4	21.9 MB
13. 16 RNN B V4 Final-wsif3p5t7CI.mp4	22.1 MB
17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.mp4	22.6 MB
16. 18 RNN Example V5 Final-MDLk3fhpTx0.mp4	23.2 MB
03. 02 RNN History V4 Final-HbxAnYUfRnc.mp4	25.4 MB
05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.mp4	27.0 MB
06. 07 FeedForward B V3-kTYbTVh1d0k.mp4	27.9 MB
08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.mp4	36.5 MB
25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.mp4	37.9 MB
10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.mp4	38.9 MB
/Part 07-Module 01-Lesson 02_Implementing Gradient Descent/
06. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt	1.4 KB
02. Gradient Descent-29PmNG7fuuM.zh-CN.vtt	1.4 KB
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02. Gradient Descent-29PmNG7fuuM.en.vtt	1.6 KB
06. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt	1.7 KB
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index.html	4.3 KB
09. Further Reading.html	5.9 KB
03. Gradient Descent The Math.html	6.1 KB
01. Mean Squared Error Function.html	6.2 KB
03. Gradient Descent-Math-7sxA5Ap8AWM.zh-CN.vtt	9.7 KB
04. Gradient Descent The Code.html	11.0 KB
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03. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt	11.1 KB
02. Gradient Descent.html	12.9 KB
07. Backpropagation.html	19.8 KB
06. Multilayer Perceptrons.html	21.5 KB
08. Implementing Backpropagation.html	21.9 KB
05. Implementing Gradient Descent.html	27.3 KB
02. Gradient Descent-29PmNG7fuuM.mp4	2.6 MB
06. Multilayer perceptrons-Rs9petvTBLk.mp4	3.0 MB
07. Backpropagation-MZL97-2joxQ.mp4	3.6 MB
03. Gradient Descent-Math-7sxA5Ap8AWM.mp4	11.8 MB
/Part 08-Module 01-Lesson 06_Transfer Learning in TensorFlow/
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07. Building The Classifier-pPHiVddBY0Q.zh-CN.vtt	1.6 KB
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06. Data Preparation-WEtKkHlhhZA.pt-BR.vtt	3.1 KB
08. Building The Classifier-6ifxRQ_gL7w.pt-BR.vtt	3.2 KB
06. Data Preparation-WEtKkHlhhZA.zh-CN.vtt	3.4 KB
09. Training The Classifier-b7Fy3cIoJ1Y.en.vtt	3.4 KB
06. Data Preparation-WEtKkHlhhZA.en.vtt	3.6 KB
08. Building The Classifier-6ifxRQ_gL7w.en.vtt	3.7 KB
index.html	4.3 KB
10. Training And Testing-NLPtmQjGYCA.zh-CN.vtt	4.8 KB
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03. VGGNet.html	5.7 KB
05. Data Preparation.html	5.7 KB
04. VGGNet Solution.html	5.7 KB
06. Data Preparation Solution.html	5.7 KB
09. Training.html	5.7 KB
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07. Classifier.html	5.7 KB
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08. Classifier Solution.html	5.7 KB
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03. Building VGGNet-615SslQiGvo.pt-BR.vtt	6.4 KB
03. Building VGGNet-615SslQiGvo.zh-CN.vtt	6.4 KB
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02. Transfer Learning--WmQwYr0DYjY.zh-CN.vtt	6.6 KB
02. Transfer Learning--WmQwYr0DYjY.en.vtt	7.1 KB
05. Data Preparation-WfsDMq-b3y4.en.vtt	7.2 KB
01. Transfer Learning Intro.html	7.3 KB
03. Building VGGNet-615SslQiGvo.en.vtt	7.7 KB
07. Building The Classifier-pPHiVddBY0Q.mp4	2.1 MB
04. Pretrained VGGNet-BpzI6Svmuv8.mp4	3.3 MB
08. Building The Classifier-6ifxRQ_gL7w.mp4	4.2 MB
09. Training The Classifier-b7Fy3cIoJ1Y.mp4	4.4 MB
06. Data Preparation-WEtKkHlhhZA.mp4	4.6 MB
10. Training And Testing-NLPtmQjGYCA.mp4	8.6 MB
02. Transfer Learning--WmQwYr0DYjY.mp4	8.7 MB
03. Building VGGNet-615SslQiGvo.mp4	9.3 MB
05. Data Preparation-WfsDMq-b3y4.mp4	9.7 MB
/Part 03-Module 05-Lesson 01_Transfer Learning in TensorFlow/
07. Building The Classifier-pPHiVddBY0Q.pt-BR.vtt	1.4 KB
07. Building The Classifier-pPHiVddBY0Q.zh-CN.vtt	1.6 KB
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04. VGGNet Solution.html	5.7 KB
06. Data Preparation Solution.html	5.7 KB
09. Training.html	5.7 KB
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07. Classifier.html	5.7 KB
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03. Building VGGNet-615SslQiGvo.pt-BR.vtt	6.4 KB
03. Building VGGNet-615SslQiGvo.zh-CN.vtt	6.4 KB
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02. Transfer Learning--WmQwYr0DYjY.zh-CN.vtt	6.6 KB
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05. Data Preparation-WfsDMq-b3y4.en.vtt	7.2 KB
01. Transfer Learning Intro.html	7.3 KB
03. Building VGGNet-615SslQiGvo.en.vtt	7.7 KB
07. Building The Classifier-pPHiVddBY0Q.mp4	2.1 MB
04. Pretrained VGGNet-BpzI6Svmuv8.mp4	3.3 MB
08. Building The Classifier-6ifxRQ_gL7w.mp4	4.2 MB
09. Training The Classifier-b7Fy3cIoJ1Y.mp4	4.4 MB
06. Data Preparation-WEtKkHlhhZA.mp4	4.6 MB
10. Training And Testing-NLPtmQjGYCA.mp4	8.6 MB
02. Transfer Learning--WmQwYr0DYjY.mp4	8.7 MB
03. Building VGGNet-615SslQiGvo.mp4	9.3 MB
05. Data Preparation-WfsDMq-b3y4.mp4	9.7 MB
/Part 09-Module 01-Lesson 04_Hyperparameters/
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02. Introduction-erwnzFD7AeE.zh-CN.vtt	2.1 KB
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05. Minibatch Size-GrrO1NFxaW8.pt-BR.vtt	4.9 KB
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02. Introduction.html	5.7 KB
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05. Minibatch Size.html	6.0 KB
03. Learning Rate.html	6.3 KB
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04. Learning Rate.html	7.5 KB
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06. Number Of Iterations-TTdHpSb4DV8.mp4	1.5 MB
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05. Minibatch Size-GrrO1NFxaW8.mp4	5.0 MB
03. Learning Rate-HLMjeDez7ps.mp4	10.1 MB
/Part 04-Module 02-Lesson 02_Hyperparameters/
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08. RNN Hyperparameters-yQvnv7l_aUo.pt-BR.vtt	3.8 KB
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05. Minibatch Size-GrrO1NFxaW8.zh-CN.vtt	4.9 KB
05. Minibatch Size-GrrO1NFxaW8.pt-BR.vtt	4.9 KB
05. Minibatch Size-GrrO1NFxaW8.en.vtt	5.5 KB
02. Introduction.html	5.7 KB
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04. Learning Rate.html	7.5 KB
06. Number of Training Iterations Epochs.html	8.5 KB
03. Learning Rate-HLMjeDez7ps.zh-CN.vtt	10.4 KB
03. Learning Rate-HLMjeDez7ps.pt-BR.vtt	10.5 KB
03. Learning Rate-HLMjeDez7ps.en.vtt	12.0 KB
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06. Number Of Iterations-TTdHpSb4DV8.mp4	1.5 MB
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07. Number Of Hidden Units Layers-IkGAIQH5wH8.mp4	3.6 MB
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05. Minibatch Size-GrrO1NFxaW8.mp4	5.0 MB
03. Learning Rate-HLMjeDez7ps.mp4	10.1 MB
/Part 03-Module 01-Lesson 03_Hyperparameters/
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05. Minibatch Size-GrrO1NFxaW8.mp4	5.0 MB
03. Learning Rate-HLMjeDez7ps.mp4	10.1 MB
/img/
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/Part 09-Module 01-Lesson 06_Sentiment Prediction RNN/
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06. Training The Network-nknJ3Xu3ld0.pt-BR.vtt	5.8 KB
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06. Training The Network-nknJ3Xu3ld0.en.vtt	6.5 KB
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02. Sentiment Prediction-uGN3rZJRiMY.zh-CN.vtt	8.2 KB
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05. Building The RNN 1-XTD6slf64fM.mp4	20.0 MB
07. Sentiment RNN 2-V9YGGjmoHS0.mp4	24.2 MB
/Part 03-Module 04-Lesson 03_Sentiment Prediction RNN/
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06. Training The Network-nknJ3Xu3ld0.pt-BR.vtt	5.8 KB
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05. Building The RNN 1-XTD6slf64fM.mp4	20.0 MB
07. Sentiment RNN 2-V9YGGjmoHS0.mp4	24.2 MB
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/Part 02-Module 01-Lesson 01_Model Evaluation and Validation/
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05. Regression-Metrics-906P4BPnl9A.zh-CN.vtt	3.7 KB
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03. Confusion Matrix-Question-9GLNjmMUB_4.pt-BR.vtt	4.9 KB
03. Confusion Matrix-Question-9GLNjmMUB_4.zh-CN.vtt	5.1 KB
07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.zh-CN.vtt	5.4 KB
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06. Types of Errors.html	5.5 KB
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07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.pt-BR.vtt	5.9 KB
04. Accuracy.html	6.1 KB
06. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt	6.2 KB
01. Introduction.html	6.2 KB
07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt	6.3 KB
06. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt	6.4 KB
03. Confusion Matrix.html	6.9 KB
02. Testing-gmxGRJSKEb0.zh-CN.vtt	6.9 KB
06. 04 L Types Of Errors-Twf1qnPZeSY.en-US.vtt	7.1 KB
07. Model Complexity Graph.html	7.3 KB
02. Testing-gmxGRJSKEb0.pt-BR.vtt	7.5 KB
02. Testing-gmxGRJSKEb0.en-US.vtt	7.8 KB
04. Accuracy Question-AmFoZTf-Hb0.mp4	1.7 MB
08. K Fold Cross Validation-dRtgSJgSt_I.mp4	2.9 MB
05. Regression-Metrics-906P4BPnl9A.mp4	3.5 MB
03. Confusion Matrix-Question-9GLNjmMUB_4.mp4	5.3 MB
07. Model Complexity Graph-Question-YS5OQCA5cLY.mp4	5.7 MB
02. Testing-gmxGRJSKEb0.mp4	5.9 MB
06. 04 L Types Of Errors-Twf1qnPZeSY.mp4	6.9 MB
07. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4	9.7 MB
/Part 02-Module 05-Lesson 04_Image Classification/
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01. Project Intro-awEYy2Df3hg.en.vtt	2.1 KB
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index.html	3.8 KB
01. Introduction to the Project.html	5.1 KB
Project Description - Image Classification.html	6.4 KB
Project Rubric - Image Classification.html	10.6 KB
01. Project Intro-awEYy2Df3hg.mp4	3.0 MB
/Part 11-Module 01-Lesson 01_Introduction to RL/
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04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt	2.1 KB
05. Resources-_YPqfAnCqtk.en.vtt	2.1 KB
01. Introduction-6jSFl5kxIBs.zh-CN.vtt	2.2 KB
04. OpenAI Gym-MktEOWp3QLg.en.vtt	2.3 KB
05. Resources-_YPqfAnCqtk.pt-BR.vtt	2.4 KB
01. Introduction-6jSFl5kxIBs.en.vtt	2.5 KB
01. Introduction-6jSFl5kxIBs.pt-BR.vtt	2.5 KB
04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt	2.5 KB
02. Applications-CV6B84mKRNM.zh-CN.vtt	3.0 KB
02. Applications-CV6B84mKRNM.en.vtt	3.3 KB
02. Applications-CV6B84mKRNM.pt-BR.vtt	3.5 KB
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03. The Setting.html	5.2 KB
01. Introduction.html	5.6 KB
06. Reference Guide.html	5.6 KB
05. Resources.html	6.0 KB
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02. Applications.html	7.3 KB
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01. Introduction-6jSFl5kxIBs.mp4	5.4 MB
05. Resources-_YPqfAnCqtk.mp4	7.3 MB
03. The Setting-nh8Gwdu19nc.mp4	8.1 MB
02. Applications-CV6B84mKRNM.mp4	8.9 MB
04. OpenAI Gym-MktEOWp3QLg.mp4	9.9 MB
/Part 03-Module 01-Lesson 01_Intro to Recurrent Neural Networks/
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03. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt	3.1 KB
12. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt	3.1 KB
09. LSTM Cell Solution-X4uA0dq_4jA.pt-BR.vtt	3.1 KB
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12. Output And Loss Solutions-CT8hcU7FmGc.en.vtt	3.5 KB
03. Character-Wise RNN-dXl3eWCGLdU.pt-BR.vtt	3.7 KB
07. Batching Data Solution-o3nBxHJLQcc.zh-CN.vtt	3.8 KB
01. Intro To RNNs-64HSG6HAfEI.zh-CN.vtt	4.0 KB
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01. Intro To RNNs-64HSG6HAfEI.pt.vtt	4.5 KB
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10. RNN Output-RkanDkcrTxs.zh-CN.vtt	5.3 KB
08. LSTM Cell-ajC-5uWB8S4.zh-CN.vtt	5.4 KB
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06. Implementing a Character-wise RNN-KPCMn_jg2oY.zh-CN.vtt	8.9 KB
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06. Implementing a Character-wise RNN-KPCMn_jg2oY.pt-BR.vtt	10.9 KB
04. Sequence-Batching-Z4OiyU0Cldg.mp4	2.4 MB
03. Character-Wise RNN-dXl3eWCGLdU.mp4	3.0 MB
09. LSTM Cell Solution-X4uA0dq_4jA.mp4	3.7 MB
01. Intro To RNNs-64HSG6HAfEI.mp4	4.2 MB
11. Network Loss-itu-uNK4brc.mp4	4.5 MB
12. Output And Loss Solutions-CT8hcU7FmGc.mp4	4.9 MB
07. Batching Data Solution-o3nBxHJLQcc.mp4	5.3 MB
02. LSTMs-RYbSHogZetc.mp4	7.2 MB
13. Build The Network-RVNjDlWTBQU.mp4	7.4 MB
08. LSTM Cell-ajC-5uWB8S4.mp4	8.2 MB
10. RNN Output-RkanDkcrTxs.mp4	9.4 MB
14. Build The Network And Results-hu8iMMqajmQ.mp4	13.9 MB
06. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4	14.3 MB
/Part 09-Module 01-Lesson 03_Implementation of RNN and LSTM/
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10. Network Loss-itu-uNK4brc.zh-CN.vtt	2.8 KB
08. LSTM Cell Solution-X4uA0dq_4jA.zh-CN.vtt	2.9 KB
02. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt	3.1 KB
11. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt	3.1 KB
08. LSTM Cell Solution-X4uA0dq_4jA.pt-BR.vtt	3.1 KB
10. Network Loss-itu-uNK4brc.en.vtt	3.2 KB
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10. Network Loss-itu-uNK4brc.pt-BR.vtt	3.2 KB
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03. Sequence-Batching-Z4OiyU0Cldg.mp4	2.4 MB
02. Character-Wise RNN-dXl3eWCGLdU.mp4	3.0 MB
08. LSTM Cell Solution-X4uA0dq_4jA.mp4	3.7 MB
10. Network Loss-itu-uNK4brc.mp4	4.5 MB
11. Output And Loss Solutions-CT8hcU7FmGc.mp4	4.9 MB
06. Batching Data Solution-o3nBxHJLQcc.mp4	5.3 MB
12. Build The Network-RVNjDlWTBQU.mp4	7.4 MB
07. LSTM Cell-ajC-5uWB8S4.mp4	8.2 MB
09. RNN Output-RkanDkcrTxs.mp4	9.4 MB
13. Build The Network And Results-hu8iMMqajmQ.mp4	13.9 MB
05. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4	14.3 MB
/Part 01-Module 03-Lesson 03_Your first neural network/
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01. Introduction to the Project-dOwEDeJp8yw.zh-CN.vtt	2.4 KB
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/Part 08-Module 01-Lesson 05_Autoencoders/
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03. A Simple Autoencoder.html	5.2 KB
02. Autoencoders.html	5.3 KB
05. Convolutional Autoencoders.html	5.4 KB
04. Simple Autoencoder Solution.html	5.4 KB
06. Convolutional Autoencoders Solution.html	5.5 KB
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04. Simple Autoencoder Solution-Nv_D6DHfEk8.pt-BR.vtt	6.6 KB
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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.zh-CN.vtt	7.2 KB
04. Simple Autoencoder Solution-Nv_D6DHfEk8.en.vtt	7.2 KB
06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.pt-BR.vtt	8.0 KB
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02. Autoencoders-ar5Iyx68cWc.mp4	2.3 MB
04. Simple Autoencoder Solution-Nv_D6DHfEk8.mp4	9.0 MB
06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.mp4	13.3 MB
03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.mp4	16.4 MB
05. Convolutional Autoencoders-18SZVRaumGs.mp4	22.6 MB
/Part 03-Module 08-Lesson 02_Autoencoders/
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03. A Simple Autoencoder.html	5.2 KB
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04. Simple Autoencoder Solution.html	5.4 KB
06. Convolutional Autoencoders Solution.html	5.5 KB
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04. Simple Autoencoder Solution-Nv_D6DHfEk8.pt-BR.vtt	6.6 KB
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06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.zh-CN.vtt	7.2 KB
04. Simple Autoencoder Solution-Nv_D6DHfEk8.en.vtt	7.2 KB
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02. Autoencoders-ar5Iyx68cWc.mp4	2.3 MB
04. Simple Autoencoder Solution-Nv_D6DHfEk8.mp4	9.0 MB
06. Convolutional Autoencoder Solutions-w3iPs6YnqmY.mp4	13.3 MB
03. A-Simple-Autoencoders 21718-lXGdkCT8E1c.mp4	16.4 MB
05. Convolutional Autoencoders-18SZVRaumGs.mp4	22.6 MB
/Part 03-Module 02-Lesson 01_Embeddings and Word2vec/
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09. Training Results-uISA5ns47s8.zh-CN.vtt	3.9 KB
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06. Building The Network-fhSb5c6UX6M.zh-CN.vtt	4.2 KB
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06. Building The Network-fhSb5c6UX6M.pt-BR.vtt	5.0 KB
03. Subsampling Solution-MAUM_mV_lj8.zh-CN.vtt	5.2 KB
06. Building The Network-fhSb5c6UX6M.en.vtt	5.3 KB
04. Making Batches.html	5.6 KB
05. Batches Solution.html	5.6 KB
09. Training Results.html	5.6 KB
07. Negative Sampling.html	5.6 KB
06. Building the Network.html	5.6 KB
03. Subsampling Solution.html	5.6 KB
02. Implementing Word2Vec.html	5.7 KB
08. Building the Network Solution.html	5.7 KB
03. Subsampling Solution-MAUM_mV_lj8.en.vtt	6.1 KB
03. Subsampling Solution-MAUM_mV_lj8.pt-BR.vtt	6.4 KB
01. Embeddings Intro.html	7.1 KB
02. Implementing Word2Vec-7M431_f9HgE.zh-CN.vtt	14.6 KB
02. Implementing Word2Vec-7M431_f9HgE.en.vtt	17.3 KB
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05. Batches Solution-DdfR0RjSC-Q.mp4	4.1 MB
07. Negative Sampling-gW17AHBKbHY.mp4	4.4 MB
08. Building The Network Solution-pkBAhQ2Ki-8.mp4	7.7 MB
04. Making Batches-jx7qwdw-94k.mp4	8.1 MB
06. Building The Network-fhSb5c6UX6M.mp4	8.5 MB
03. Subsampling Solution-MAUM_mV_lj8.mp4	10.1 MB
09. Training Results-uISA5ns47s8.mp4	10.3 MB
02. Implementing Word2Vec-7M431_f9HgE.mp4	24.5 MB
/Part 09-Module 01-Lesson 05_Embeddings and Word2vec/
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08. Building The Network Solution-pkBAhQ2Ki-8.zh-CN.vtt	3.4 KB
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08. Building The Network Solution-pkBAhQ2Ki-8.pt-BR.vtt	4.1 KB
08. Building The Network Solution-pkBAhQ2Ki-8.en.vtt	4.2 KB
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06. Building The Network-fhSb5c6UX6M.zh-CN.vtt	4.2 KB
09. Training Results-uISA5ns47s8.en.vtt	4.5 KB
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04. Making Batches-jx7qwdw-94k.en.vtt	4.6 KB
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06. Building The Network-fhSb5c6UX6M.pt-BR.vtt	5.0 KB
03. Subsampling Solution-MAUM_mV_lj8.zh-CN.vtt	5.2 KB
06. Building The Network-fhSb5c6UX6M.en.vtt	5.3 KB
04. Making Batches.html	5.6 KB
05. Batches Solution.html	5.6 KB
09. Training Results.html	5.6 KB
07. Negative Sampling.html	5.6 KB
06. Building the Network.html	5.6 KB
03. Subsampling Solution.html	5.6 KB
02. Implementing Word2Vec.html	5.7 KB
08. Building the Network Solution.html	5.7 KB
03. Subsampling Solution-MAUM_mV_lj8.en.vtt	6.1 KB
03. Subsampling Solution-MAUM_mV_lj8.pt-BR.vtt	6.4 KB
01. Embeddings Intro.html	7.1 KB
02. Implementing Word2Vec-7M431_f9HgE.zh-CN.vtt	14.6 KB
02. Implementing Word2Vec-7M431_f9HgE.en.vtt	17.3 KB
02. Implementing Word2Vec-7M431_f9HgE.pt-BR.vtt	17.6 KB
05. Batches Solution-DdfR0RjSC-Q.mp4	4.1 MB
07. Negative Sampling-gW17AHBKbHY.mp4	4.4 MB
08. Building The Network Solution-pkBAhQ2Ki-8.mp4	7.7 MB
04. Making Batches-jx7qwdw-94k.mp4	8.1 MB
06. Building The Network-fhSb5c6UX6M.mp4	8.5 MB
03. Subsampling Solution-MAUM_mV_lj8.mp4	10.1 MB
09. Training Results-uISA5ns47s8.mp4	10.3 MB
02. Implementing Word2Vec-7M431_f9HgE.mp4	24.5 MB
/Part 06-Module 01-Lesson 04_Jupyter Notebooks/
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02. Jupyter-qiYDWFLyXvg.zh-CN.vtt	2.7 KB
02. Jupyter-qiYDWFLyXvg.en.vtt	2.8 KB
02. Jupyter-qiYDWFLyXvg.ar.vtt	3.5 KB
index.html	4.4 KB
12. Finishing up.html	5.9 KB
03. Installing Jupyter Notebook.html	5.9 KB
01. Instructor.html	6.0 KB
08. Keyboard shortcuts.html	6.1 KB
06. Code cells.html	6.2 KB
10. Converting notebooks.html	7.2 KB
11. Creating a slideshow.html	8.1 KB
05. Notebook interface.html	9.6 KB
07. Markdown cells.html	9.7 KB
09. Magic keywords.html	10.1 KB
04. Launching the notebook server.html	10.9 KB
02. What are Jupyter notebooks.html	12.6 KB
02. Jupyter-qiYDWFLyXvg.mp4	7.5 MB
/Part 01-Module 01-Lesson 03_Jupyter Notebooks/
02. Jupyter-qiYDWFLyXvg.pt-BR.vtt	2.5 KB
02. Jupyter-qiYDWFLyXvg.zh-CN.vtt	2.7 KB
02. Jupyter-qiYDWFLyXvg.en.vtt	2.8 KB
02. Jupyter-qiYDWFLyXvg.ar.vtt	3.5 KB
index.html	4.4 KB
12. Finishing up.html	5.9 KB
03. Installing Jupyter Notebook.html	5.9 KB
01. Instructor.html	6.0 KB
08. Keyboard shortcuts.html	6.1 KB
06. Code cells.html	6.2 KB
10. Converting notebooks.html	7.2 KB
11. Creating a slideshow.html	8.1 KB
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07. Markdown cells.html	9.7 KB
09. Magic keywords.html	10.1 KB
04. Launching the notebook server.html	10.9 KB
02. What are Jupyter notebooks.html	12.6 KB
02. Jupyter-qiYDWFLyXvg.mp4	7.5 MB
/Part 03-Module 06-Lesson 01_Sequence to Sequence/
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03. Applications seq2seq-tDJBDwriJYQ.pt-BR.vtt	2.6 KB
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05. Architecture in More Depth-rdAo4MqLbEk.zh-CN.vtt	4.7 KB
04. Architecture encoder decoder-dkHdEAJnV_w.zh-CN.vtt	4.9 KB
04. Architecture encoder decoder-dkHdEAJnV_w.pt-BR.vtt	5.0 KB
05. Architecture in More Depth-rdAo4MqLbEk.pt-BR.vtt	5.1 KB
02. Jay's Introduction-HPOzAlXhuxQ.zh-CN.vtt	5.3 KB
05. Architecture in More Depth-rdAo4MqLbEk.en.vtt	5.3 KB
04. Architecture encoder decoder-dkHdEAJnV_w.en.vtt	5.6 KB
02. Jay's Introduction-HPOzAlXhuxQ.pt-BR.vtt	5.7 KB
03. Applications.html	5.7 KB
02. Jay Introduction.html	5.8 KB
04. Architectures.html	5.8 KB
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02. Jay's Introduction-HPOzAlXhuxQ.en.vtt	5.9 KB
10. Sequence to Sequence in TensorFlow.html	5.9 KB
09. Further Reading.html	6.2 KB
06. Preprocessing.html	6.3 KB
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06. Preprocessing-ktQW6p9pOS4.zh-CN.vtt	6.6 KB
06. Preprocessing-ktQW6p9pOS4.pt-BR.vtt	7.0 KB
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07. Sequence to sequence in TensorFlow.html	8.7 KB
08. Inputs.html	16.2 KB
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04. Architecture encoder decoder-dkHdEAJnV_w.mp4	4.7 MB
05. Architecture in More Depth-rdAo4MqLbEk.mp4	5.6 MB
02. Jay's Introduction-HPOzAlXhuxQ.mp4	6.1 MB
06. Preprocessing-ktQW6p9pOS4.mp4	9.3 MB
/Part 03-Module 07-Lesson 01_Reinforcement Learning/
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02. 01 Q-Learning-Npu9gyD6-4o.pt-BR.vtt	2.8 KB
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03. Q-Learning.html	5.1 KB
02. Reinforcement Learning.html	5.1 KB
04. Deep Q-Learning.html	6.1 KB
01. Reinforcement Learning Lesson.html	6.2 KB
03. 02 Q-Learning-WQgdnzzhSLM.zh-CN.vtt	8.8 KB
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02. 01 Q-Learning-Npu9gyD6-4o.mp4	3.2 MB
03. 02 Q-Learning-WQgdnzzhSLM.mp4	11.1 MB
/Part 10-Module 01-Lesson 02_Deep Convolutional GANs/
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06. Generator Solution-jyPwUEZg05Q.zh-CN.vtt	3.4 KB
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10. Hyperparameters Solution-Rt8MlVDtpi8.pt-BR.vtt	3.6 KB
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06. Generator Solution-jyPwUEZg05Q.en.vtt	3.9 KB
10. Hyperparameters Solution-Rt8MlVDtpi8.en.vtt	4.3 KB
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09. Building And Training The Network-nXKk9GI4X14.pt-BR.vtt	4.7 KB
09. Building And Training The Network-nXKk9GI4X14.zh-CN.vtt	5.3 KB
07. Discriminator.html	5.7 KB
02. DCGAN Architecture.html	5.8 KB
06. Generator Solution.html	5.8 KB
08. Discriminator Solution.html	5.8 KB
05. DCGAN and the Generator.html	5.8 KB
10. Hyperparameter Solutions.html	5.8 KB
09. Building and Training the Network.html	5.9 KB
09. Building And Training The Network-nXKk9GI4X14.en.vtt	6.3 KB
04. DCGAN Implementation.html	6.3 KB
02. Deconvolution-sX_AxtB6CHI.zh-CN.vtt	6.6 KB
03. Batch Normalization.html	6.7 KB
01. Deep Convolutional GANs.html	6.9 KB
02. Deconvolution-sX_AxtB6CHI.pt-BR.vtt	7.2 KB
02. Deconvolution-sX_AxtB6CHI.en.vtt	7.4 KB
05. DCGAN And The Generator-CH6BxLTKt7s.pt-BR.vtt	9.3 KB
05. DCGAN And The Generator-CH6BxLTKt7s.zh-CN.vtt	9.9 KB
05. DCGAN And The Generator-CH6BxLTKt7s.en.vtt	11.3 KB
07. Discriminator-XRqOUbf96eI.mp4	3.4 MB
08. Discriminator Solution-ffPWI2yJscw.mp4	4.1 MB
10. Hyperparameters Solution-Rt8MlVDtpi8.mp4	7.3 MB
06. Generator Solution-jyPwUEZg05Q.mp4	7.6 MB
09. Building And Training The Network-nXKk9GI4X14.mp4	8.5 MB
02. Deconvolution-sX_AxtB6CHI.mp4	8.7 MB
05. DCGAN And The Generator-CH6BxLTKt7s.mp4	16.8 MB
/Part 04-Module 02-Lesson 03_Deep Convolutional GANs/
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07. Discriminator-XRqOUbf96eI.zh-CN.vtt	2.7 KB
08. Discriminator Solution-ffPWI2yJscw.pt-BR.vtt	2.8 KB
08. Discriminator Solution-ffPWI2yJscw.zh-CN.vtt	3.1 KB
07. Discriminator-XRqOUbf96eI.en.vtt	3.2 KB
06. Generator Solution-jyPwUEZg05Q.pt-BR.vtt	3.2 KB
06. Generator Solution-jyPwUEZg05Q.zh-CN.vtt	3.4 KB
08. Discriminator Solution-ffPWI2yJscw.en.vtt	3.4 KB
10. Hyperparameters Solution-Rt8MlVDtpi8.pt-BR.vtt	3.6 KB
10. Hyperparameters Solution-Rt8MlVDtpi8.zh-CN.vtt	3.7 KB
06. Generator Solution-jyPwUEZg05Q.en.vtt	3.9 KB
10. Hyperparameters Solution-Rt8MlVDtpi8.en.vtt	4.3 KB
index.html	4.3 KB
09. Building And Training The Network-nXKk9GI4X14.pt-BR.vtt	4.7 KB
09. Building And Training The Network-nXKk9GI4X14.zh-CN.vtt	5.3 KB
07. Discriminator.html	5.7 KB
02. DCGAN Architecture.html	5.8 KB
06. Generator Solution.html	5.8 KB
08. Discriminator Solution.html	5.8 KB
05. DCGAN and the Generator.html	5.8 KB
10. Hyperparameter Solutions.html	5.8 KB
09. Building and Training the Network.html	5.9 KB
09. Building And Training The Network-nXKk9GI4X14.en.vtt	6.3 KB
04. DCGAN Implementation.html	6.3 KB
02. Deconvolution-sX_AxtB6CHI.zh-CN.vtt	6.6 KB
03. Batch Normalization.html	6.7 KB
01. Deep Convolutional GANs.html	6.9 KB
02. Deconvolution-sX_AxtB6CHI.pt-BR.vtt	7.2 KB
02. Deconvolution-sX_AxtB6CHI.en.vtt	7.4 KB
05. DCGAN And The Generator-CH6BxLTKt7s.pt-BR.vtt	9.3 KB
05. DCGAN And The Generator-CH6BxLTKt7s.zh-CN.vtt	9.9 KB
05. DCGAN And The Generator-CH6BxLTKt7s.en.vtt	11.3 KB
07. Discriminator-XRqOUbf96eI.mp4	3.4 MB
08. Discriminator Solution-ffPWI2yJscw.mp4	4.1 MB
10. Hyperparameters Solution-Rt8MlVDtpi8.mp4	7.3 MB
06. Generator Solution-jyPwUEZg05Q.mp4	7.6 MB
09. Building And Training The Network-nXKk9GI4X14.mp4	8.5 MB
02. Deconvolution-sX_AxtB6CHI.mp4	8.7 MB
05. DCGAN And The Generator-CH6BxLTKt7s.mp4	16.8 MB
/Part 04-Module 02-Lesson 05_Semi-Supervised Learning/
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11. Model Optimizer Solution-_Qhz9SbR7xY.en.vtt	3.4 KB
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08. Training The Network -P-LXQPVXl4A.zh-CN.vtt	4.3 KB
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04. Data Prep-P5hOx09mwaM.pt-BR.vtt	4.7 KB
03. Introducing Semi-Supervised Learning-tnCClNy5z5c.zh-CN.vtt	4.7 KB
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10. Model Loss Solution-r3DtohmychE.pt-BR.vtt	6.9 KB
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11. Model Optimizer Solution-_Qhz9SbR7xY.mp4	4.3 MB
08. Training The Network -P-LXQPVXl4A.mp4	5.7 MB
03. Introducing Semi-Supervised Learning-tnCClNy5z5c.mp4	6.2 MB
04. Data Prep-P5hOx09mwaM.mp4	6.4 MB
12. Trained Semi-Supervised GAN-9yWYZDX8-O8.mp4	7.5 MB
09. Discriminator Solution-_X8ssUzu_Bo.mp4	7.8 MB
10. Model Loss Solution-r3DtohmychE.mp4	9.3 MB
02. Semi-Supervised Learning-_LRpHPxZaX0.mp4	10.6 MB
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06. Model Loss Exercise-W7TawMNxBds.mp4	16.6 MB
05. Building The Generator And Discriminator-OWytckbbeGQ.mp4	18.8 MB
/Part 10-Module 01-Lesson 03_Semi-Supervised Learning/
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11. Model Optimizer Solution-_Qhz9SbR7xY.mp4	4.3 MB
08. Training The Network -P-LXQPVXl4A.mp4	5.7 MB
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04. Data Prep-P5hOx09mwaM.mp4	6.4 MB
12. Trained Semi-Supervised GAN-9yWYZDX8-O8.mp4	7.5 MB
09. Discriminator Solution-_X8ssUzu_Bo.mp4	7.8 MB
10. Model Loss Solution-r3DtohmychE.mp4	9.3 MB
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06. Model Loss Exercise-W7TawMNxBds.mp4	16.6 MB
05. Building The Generator And Discriminator-OWytckbbeGQ.mp4	18.8 MB
/Part 06-Module 01-Lesson 03_Anaconda/
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02. Why Anaconda-VXukXZv7SCQ.en.vtt	3.5 KB
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02. Why Anaconda-VXukXZv7SCQ.ar.vtt	5.1 KB
01. Instructor.html	5.8 KB
02. Introduction.html	6.3 KB
08. Best practices.html	7.1 KB
09. On Python versions at Udacity.html	7.2 KB
07. More environment actions.html	7.3 KB
04. Installing Anaconda.html	7.8 KB
05. Managing packages.html	8.5 KB
06. Managing environments.html	8.9 KB
03. What is Anaconda.html	10.8 KB
02. Why Anaconda-VXukXZv7SCQ.mp4	10.8 MB
/Part 01-Module 01-Lesson 02_Anaconda/
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02. Why Anaconda-VXukXZv7SCQ.ar.vtt	5.1 KB
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05. Managing packages.html	8.5 KB
06. Managing environments.html	8.9 KB
03. What is Anaconda.html	10.8 KB
02. Why Anaconda-VXukXZv7SCQ.mp4	10.8 MB
/Part 03-Module 06-Lesson 02_Siraj's Chatbot/
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01. How to Make a Chatbot.html	6.8 KB
01. How to Make a Chatbot - Intro to Deep Learning #12-t5qgjJIBy9g.en.vtt	17.2 KB
01. How to Make a Chatbot - Intro to Deep Learning #12-t5qgjJIBy9g.mp4	49.9 MB
/Part 03-Module 02-Lesson 03_Q&A with FloydHub Founders/
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01. FloydHub QA.html	4.6 KB
01. Floyd QA-KUc59DPfBeo.pt-BR.vtt	49.6 KB
01. Floyd QA-KUc59DPfBeo.en-US.vtt	60.4 KB
01. Floyd QA-KUc59DPfBeo.mp4	226.1 MB
/Part 03-Module 02-Lesson 02_Siraj's Style Transfer/
index.html	3.6 KB
01. How to Generate Art.html	6.8 KB
01. How to Generate Art - Intro to Deep Learning #8-Oex0eWoU7AQ.en.vtt	15.1 KB
01. How to Generate Art - Intro to Deep Learning #8-Oex0eWoU7AQ.mp4	43.2 MB
/Part 04-Module 01-Lesson 02_Siraj's Video Generation/
index.html	3.6 KB
01. How to Generate Video.html	7.5 KB
01. How to Generate Video - Intro to Deep Learning #15--E2N1kQc8MM.en.vtt	18.5 KB
01. How to Generate Video - Intro to Deep Learning #15--E2N1kQc8MM.mp4	49.1 MB
/Part 11-Module 02-Lesson 01_Teach a Quadcopter How to Fly/
index.html	3.6 KB
01. Project Description.html	6.1 KB
/Part 03-Module 08-Lesson 01_Siraj's Image Generation/
index.html	3.6 KB
01. How to Generate Images.html	7.0 KB
01. How to Generate Images - Intro to Deep Learning #14-3-UDwk1U77s.en.vtt	16.9 KB
01. How to Generate Images - Intro to Deep Learning #14-3-UDwk1U77s.mp4	53.2 MB
/Part 08-Module 02-Lesson 01_CNN Project Dog Breed Classifier/
index.html	3.6 KB
01. Project Description.html	6.6 KB
/Part 03-Module 07-Lesson 02_Siraj's Reinforcement Learning/
index.html	3.6 KB
01. How to Win Slot Machines.html	6.9 KB
01. How to Win Slot Machines - Intro to Deep Learning #13-AIeWLTUYLZQ.en.vtt	17.6 KB
01. How to Win Slot Machines - Intro to Deep Learning #13-AIeWLTUYLZQ.mp4	54.5 MB
/Part 04-Module 02-Lesson 01_Siraj's One-Shot Learning/
index.html	3.6 KB
01. How to Learn from Little Data.html	6.1 KB
01. How to Learn from Little Data - Intro to Deep Learning #17-tChcZpBbTTA.nl.vtt	13.8 KB
01. How to Learn from Little Data - Intro to Deep Learning #17-tChcZpBbTTA.en.vtt	15.6 KB
01. How to Learn from Little Data - Intro to Deep Learning #17-tChcZpBbTTA.mp4	37.1 MB
/Part 03-Module 04-Lesson 01_Siraj's Text Summarization/
index.html	3.6 KB
01. How to Make a Text Summarizer.html	6.7 KB
01. How to Make a Text Summarizer - Intro to Deep Learning #10-ogrJaOIuBx4.fi.vtt	17.3 KB
01. How to Make a Text Summarizer - Intro to Deep Learning #10-ogrJaOIuBx4.pt-BR.vtt	17.7 KB
01. How to Make a Text Summarizer - Intro to Deep Learning #10-ogrJaOIuBx4.en.vtt	18.3 KB
01. How to Make a Text Summarizer - Intro to Deep Learning #10-ogrJaOIuBx4.mp4	38.0 MB
/Part 03-Module 01-Lesson 02_Siraj's Stock Prediction/
index.html	3.6 KB
01. How to Predict Stock Prices Easily.html	6.4 KB
01. How to Predict Stock Prices Easily - Intro to Deep Learning #7-ftMq5ps503w.en.vtt	19.3 KB
01. How to Predict Stock Prices Easily - Intro to Deep Learning #7-ftMq5ps503w.mp4	41.3 MB
/Part 03-Module 05-Lesson 02_Siraj's Language Translation/
index.html	3.6 KB
01. How to Make a Language Translator.html	7.7 KB
01. How to Make a Language Translator - Intro to Deep Learning #11-nRBnh4qbPHI.en.vtt	14.0 KB
01. How to Make a Language Translator - Intro to Deep Learning #11-nRBnh4qbPHI.mp4	38.3 MB
/Part 05-Module 01-Lesson 01_Enroll in your next Nanodegree program/
index.html	3.6 KB
01. Enroll in your next ND program.html	8.0 KB
/Part 08-Module 01-Lesson 03_Weight Initialization/
04. Weight Initialization 3-JIQl0jMpdsI.zh-CN.vtt	3.7 KB
04. Weight Initialization 3-JIQl0jMpdsI.pt-BR.vtt	3.7 KB
index.html	4.0 KB
04. Weight Initialization 3-JIQl0jMpdsI.en.vtt	4.3 KB
04. Too Small.html	5.3 KB
02. Ones and Zeros.html	5.3 KB
05. Normal Distribution.html	5.3 KB
03. Uniform Distribution.html	5.3 KB
06. Additional Material.html	5.6 KB
01. Weight Initialization Intro.html	5.8 KB
05. Weight Initialization 4-FM6t7AsodGQ.zh-CN.vtt	7.0 KB
05. Weight Initialization 4-FM6t7AsodGQ.pt-BR.vtt	7.4 KB
03. Weight Initialization 2-BI3f0Cdc_nU.zh-CN.vtt	8.2 KB
02. Weight Initialization 1-6vXMYu_TQIA.zh-CN.vtt	8.3 KB
02. Weight Initialization 1-6vXMYu_TQIA.pt-BR.vtt	8.4 KB
05. Weight Initialization 4-FM6t7AsodGQ.en.vtt	8.5 KB
03. Weight Initialization 2-BI3f0Cdc_nU.pt-BR.vtt	8.6 KB
03. Weight Initialization 2-BI3f0Cdc_nU.en.vtt	9.7 KB
02. Weight Initialization 1-6vXMYu_TQIA.en.vtt	9.9 KB
04. Weight Initialization 3-JIQl0jMpdsI.mp4	5.7 MB
05. Weight Initialization 4-FM6t7AsodGQ.mp4	10.0 MB
02. Weight Initialization 1-6vXMYu_TQIA.mp4	11.3 MB
03. Weight Initialization 2-BI3f0Cdc_nU.mp4	11.6 MB
/Part 03-Module 04-Lesson 02_Weight Initialization/
04. Weight Initialization 3-JIQl0jMpdsI.zh-CN.vtt	3.7 KB
04. Weight Initialization 3-JIQl0jMpdsI.pt-BR.vtt	3.7 KB
index.html	4.0 KB
04. Weight Initialization 3-JIQl0jMpdsI.en.vtt	4.3 KB
04. Too Small.html	5.3 KB
02. Ones and Zeros.html	5.3 KB
05. Normal Distribution.html	5.3 KB
03. Uniform Distribution.html	5.3 KB
06. Additional Material.html	5.6 KB
01. Weight Initialization Intro.html	5.8 KB
05. Weight Initialization 4-FM6t7AsodGQ.zh-CN.vtt	7.0 KB
05. Weight Initialization 4-FM6t7AsodGQ.pt-BR.vtt	7.4 KB
03. Weight Initialization 2-BI3f0Cdc_nU.zh-CN.vtt	8.2 KB
02. Weight Initialization 1-6vXMYu_TQIA.zh-CN.vtt	8.3 KB
02. Weight Initialization 1-6vXMYu_TQIA.pt-BR.vtt	8.4 KB
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02. Weight Initialization 1-6vXMYu_TQIA.en.vtt	9.9 KB
04. Weight Initialization 3-JIQl0jMpdsI.mp4	5.7 MB
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02. Weight Initialization 1-6vXMYu_TQIA.mp4	11.3 MB
03. Weight Initialization 2-BI3f0Cdc_nU.mp4	11.6 MB
/Part 02-Module 05-Lesson 03_Siraj's Image Classification/
index.html	3.7 KB
01. On Keras.html	5.3 KB
02. How to Make an Image Classifier.html	7.5 KB
02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.en.vtt	14.6 KB
02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.pt.vtt	15.1 KB
02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.ru.vtt	23.1 KB
02. How to Make an Image Classifier - Intro to Deep Learning #6-cAICT4Al5Ow.mp4	42.6 MB
/Part 11-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/
index.html	3.7 KB
03. Mini Project.html	5.5 KB
01. Introduction.html	7.8 KB
02. Instructions.html	11.3 KB
/Part 03-Module 03-Lesson 02_Siraj's Music Generation/
index.html	3.7 KB
02. How to Succeed in any Programming Interview.html	5.1 KB
01. How to Generate Music.html	7.3 KB
02. How to Succeed in any Programming Interview-5KB5KAak6tM.en.vtt	12.4 KB
01. How to Generate Music - Intro to Deep Learning #9-4DMm5Lhey1U.en.vtt	14.9 KB
02. How to Succeed in any Programming Interview-5KB5KAak6tM.ko.vtt	17.9 KB
02. How to Succeed in any Programming Interview-5KB5KAak6tM.ru.vtt	18.2 KB
02. How to Succeed in any Programming Interview-5KB5KAak6tM.mp4	28.3 MB
01. How to Generate Music - Intro to Deep Learning #9-4DMm5Lhey1U.mp4	45.6 MB
/Part 06-Module 01-Lesson 02_Applying Deep Learning/
index.html	3.8 KB
01. Introduction.html	5.5 KB
05. Books to Read.html	6.0 KB
03. DeepTraffic.html	6.5 KB
04. Flappy Bird.html	6.9 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.en.vtt	7.5 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.zh-CN.vtt	7.6 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.pt-BR.vtt	7.8 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.en-US.vtt	7.9 KB
02. Style Transfer.html	12.4 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.mp4	40.0 MB
/Part 01-Module 01-Lesson 04_Applying Deep Learning/
index.html	3.8 KB
01. Introduction.html	5.5 KB
05. Books to Read.html	6.0 KB
03. DeepTraffic.html	6.5 KB
04. Flappy Bird.html	6.9 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.en.vtt	7.5 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.zh-CN.vtt	7.6 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.pt-BR.vtt	7.8 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.en-US.vtt	7.9 KB
02. Style Transfer.html	12.4 KB
03. Traffic Navigation with Deep Reinforcement Learning-az5ElmV4DhY.mp4	40.0 MB
/assets/css/
styles.css	3.9 KB
katex.min.css	22.1 KB
plyr.css	24.2 KB
jquery.mCustomScrollbar.min.css	42.8 KB
bootstrap.min.css	140.9 KB
/Part 03-Module 03-Lesson 01_TensorBoard/
index.html	3.9 KB
03. Name Scopes.html	5.2 KB
02. Viewing Graphs.html	5.2 KB
04. Inspecting Variables.html	5.2 KB
05. Choosing Hyperparameters.html	5.2 KB
01. TensorBoard Intro.html	6.2 KB
03. TensorBoard Graphs 2-REmz7HUj6f4.pt-BR.vtt	7.0 KB
02. TensorBoard Graphs 1-M64FWxf1yK4.pt-BR.vtt	7.4 KB
03. TensorBoard Graphs 2-REmz7HUj6f4.zh-CN.vtt	7.5 KB
02. TensorBoard Graphs 1-M64FWxf1yK4.zh-CN.vtt	7.8 KB
03. TensorBoard Graphs 2-REmz7HUj6f4.en.vtt	8.3 KB
05. TensorBoard Hyperparameters-THiwPbkjoLQ.pt-BR.vtt	8.7 KB
02. TensorBoard Graphs 1-M64FWxf1yK4.en.vtt	8.9 KB
05. TensorBoard Hyperparameters-THiwPbkjoLQ.zh-CN.vtt	9.1 KB
05. TensorBoard Hyperparameters-THiwPbkjoLQ.en.vtt	9.9 KB
04. TensorBoard Variables 1-QG41p4Wx5wc.pt-BR.vtt	11.2 KB
04. TensorBoard Variables 1-QG41p4Wx5wc.zh-CN.vtt	12.9 KB
04. TensorBoard Variables 1-QG41p4Wx5wc.en.vtt	13.9 KB
03. TensorBoard Graphs 2-REmz7HUj6f4.mp4	11.6 MB
02. TensorBoard Graphs 1-M64FWxf1yK4.mp4	12.6 MB
05. TensorBoard Hyperparameters-THiwPbkjoLQ.mp4	15.1 MB
04. TensorBoard Variables 1-QG41p4Wx5wc.mp4	19.9 MB
/assets/css/fonts/
KaTeX_Size3-Regular.woff2	3.9 KB
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KaTeX_Size2-Regular.woff2	5.6 KB
KaTeX_Size1-Regular.woff2	5.8 KB
KaTeX_Size4-Regular.woff	6.5 KB
KaTeX_Size2-Regular.woff	6.7 KB
KaTeX_Size1-Regular.woff	7.0 KB
KaTeX_Size3-Regular.ttf	8.4 KB
KaTeX_Caligraphic-Regular.woff2	10.4 KB
KaTeX_Caligraphic-Bold.woff2	10.6 KB
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KaTeX_Caligraphic-Bold.woff	12.1 KB
KaTeX_Script-Regular.woff2	12.3 KB
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KaTeX_Fraktur-Bold.woff	23.4 KB
KaTeX_Math-Italic.woff	23.8 KB
KaTeX_Script-Regular.ttf	24.9 KB
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KaTeX_Main-Regular.ttf	70.1 KB
KaTeX_AMS-Regular.ttf	71.4 KB
/Part 02-Module 02-Lesson 03_Preparing for Siraj's Lesson/
index.html	3.9 KB
01. Introduction.html	5.4 KB
05. RNNs and LSTMs.html	6.2 KB
02. Bag of Words.html	8.0 KB
03. Converting Documents to Vectors.html	8.2 KB
04. Word2vec.html	9.2 KB
/img/
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/Part 08-Module 01-Lesson 01_Cloud Computing/
index.html	4.0 KB
07. More Resources.html	5.5 KB
02. Create an AWS Account.html	6.0 KB
01. Overview.html	6.6 KB
04. Apply Credits.html	6.8 KB
03. Get Access to GPU Instances.html	8.8 KB
06. Login to the Instance.html	9.7 KB
05. Launch an Instance.html	12.8 KB
/Part 02-Module 04-Lesson 01_Cloud Computing/
index.html	4.0 KB
07. More Resources.html	5.5 KB
02. Create an AWS Account.html	6.0 KB
01. Overview.html	6.6 KB
04. Apply Credits.html	6.8 KB
03. Get Access to GPU Instances.html	8.8 KB
06. Login to the Instance.html	9.7 KB
05. Launch an Instance.html	12.8 KB
/Part 01-Module 03-Lesson 01_Matrix Math and NumPy Refresher/
04. Element-wise Matrix Operations-vjUykZyzko4.zh-CN.vtt	4.1 KB
04. Element-wise Matrix Operations-vjUykZyzko4.en.vtt	4.3 KB
04. Element-wise Matrix Operations-vjUykZyzko4.pt-BR.vtt	4.4 KB
index.html	4.4 KB
02. Data Dimensions.html	5.9 KB
09. Matrix Transposes.html	5.9 KB
04. Element-wise Matrix Operations.html	6.0 KB
01. Introduction.html	6.1 KB
06. Matrix Multiplication Part 1.html	6.2 KB
07. Matrix Multiplication Part 2.html	7.2 KB
02. Data Has Dimensions-F4NSv776X0c.zh-CN.vtt	7.8 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.zh-CN.vtt	7.9 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.pt-BR.vtt	8.1 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.en.vtt	8.1 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.zh-CN.vtt	8.8 KB
02. Data Has Dimensions-F4NSv776X0c.en.vtt	8.9 KB
08. NumPy Matrix Multiplication.html	8.9 KB
02. Data Has Dimensions-F4NSv776X0c.pt-BR.vtt	9.0 KB
05. Element-wise Operations in NumPy.html	9.4 KB
10. Transposes in NumPy.html	9.4 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.pt-BR.vtt	9.5 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.en.vtt	10.1 KB
09. Matrix Transposes-NVK5xCY3CZE.zh-CN.vtt	10.6 KB
11. NumPy Quiz.html	10.7 KB
09. Matrix Transposes-NVK5xCY3CZE.pt-BR.vtt	11.4 KB
09. Matrix Transposes-NVK5xCY3CZE.en-US.vtt	12.1 KB
03. Data in NumPy.html	13.7 KB
04. Element-wise Matrix Operations-vjUykZyzko4.mp4	4.2 MB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.mp4	6.2 MB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.mp4	7.7 MB
09. Matrix Transposes-NVK5xCY3CZE.mp4	9.5 MB
02. Data Has Dimensions-F4NSv776X0c.mp4	11.1 MB
/Part 06-Module 01-Lesson 05_Matrix Math and NumPy Refresher/
04. Element-wise Matrix Operations-vjUykZyzko4.zh-CN.vtt	4.1 KB
04. Element-wise Matrix Operations-vjUykZyzko4.en.vtt	4.3 KB
04. Element-wise Matrix Operations-vjUykZyzko4.pt-BR.vtt	4.4 KB
index.html	4.4 KB
02. Data Dimensions.html	5.9 KB
09. Matrix Transposes.html	5.9 KB
04. Element-wise Matrix Operations.html	6.0 KB
01. Introduction.html	6.1 KB
06. Matrix Multiplication Part 1.html	6.2 KB
07. Matrix Multiplication Part 2.html	7.2 KB
02. Data Has Dimensions-F4NSv776X0c.zh-CN.vtt	7.8 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.zh-CN.vtt	7.9 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.pt-BR.vtt	8.1 KB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.en.vtt	8.1 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.zh-CN.vtt	8.8 KB
02. Data Has Dimensions-F4NSv776X0c.en.vtt	8.9 KB
08. NumPy Matrix Multiplication.html	8.9 KB
02. Data Has Dimensions-F4NSv776X0c.pt-BR.vtt	9.0 KB
05. Element-wise Operations in NumPy.html	9.4 KB
10. Transposes in NumPy.html	9.4 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.pt-BR.vtt	9.5 KB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.en.vtt	10.1 KB
09. Matrix Transposes-NVK5xCY3CZE.zh-CN.vtt	10.6 KB
11. NumPy Quiz.html	10.7 KB
09. Matrix Transposes-NVK5xCY3CZE.pt-BR.vtt	11.4 KB
09. Matrix Transposes-NVK5xCY3CZE.en-US.vtt	12.1 KB
03. Data in NumPy.html	13.7 KB
04. Element-wise Matrix Operations-vjUykZyzko4.mp4	4.2 MB
06. Matrix Multiplication Part 1-JRoCFQRP4B0.mp4	6.2 MB
07. Matrix Multiplication Part 2-8jtk8BzBdj8.mp4	7.7 MB
09. Matrix Transposes-NVK5xCY3CZE.mp4	9.5 MB
02. Data Has Dimensions-F4NSv776X0c.mp4	11.1 MB
/Part 02-Module 02-Lesson 02_Intro to TFLearn/
index.html	4.2 KB
05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.zh-CN.vtt	5.8 KB
05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.en.vtt	6.4 KB
01. Welcome to this lesson!.html	6.4 KB
05. Sentiment Analysis Solution.html	6.5 KB
05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.pt-BR.vtt	6.7 KB
07. Handwritten Digit Recognition Solution.html	6.9 KB
04. TFLearn-YF7S6hi4bnc.zh-CN.vtt	7.4 KB
04. TFLearn-YF7S6hi4bnc.en-US.vtt	8.1 KB
03. Categorical Cross-Entropy.html	8.8 KB
04. Sentiment Analysis with TFLearn.html	9.1 KB
06. Handwritten Digit Recognition.html	9.6 KB
02. ReLU and Softmax Activation Functions.html	14.6 KB
04. TFLearn-YF7S6hi4bnc.mp4	12.3 MB
05. Sentiment Analysis Solution SC-s7FKYC5Zcm8.mp4	18.4 MB
/Part 08-Module 01-Lesson 02_CNNs in TensorFlow/
index.html	4.2 KB
08. CNNs - Additional Resources.html	6.1 KB
06. Solution Max Pooling Layers.html	6.4 KB
03. Solution Convolutional Layers.html	6.9 KB
05. Quiz Max Pooling Layers.html	7.6 KB
04. Max Pooling Layers.html	8.0 KB
02. Quiz Convolutional Layers.html	9.0 KB
01. Convolutional Layers.html	9.7 KB
07. CNNs in TensorFlow.html	13.0 KB
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sequence-to-sequence-high-level-encoder-decoder.png	11.8 KB
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truncated-eval.png	230.6 KB
iteration.png	247.2 KB
policy-eval.png	265.9 KB
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value-iteration.png	390.4 KB
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statevalue.png	1.0 MB
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two-layer-network.png	17.6 KB
relu-network.png	31.8 KB
dropout-node.jpeg	64.2 KB
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layers.png	293.0 KB
regularization-quiz.png	431.0 KB
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student-acceptance.png	21.0 KB
data.png	50.7 KB
summary.png	96.0 KB
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view.png	22.9 KB
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review-example.png	371.5 KB
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sequence-to-sequence-unrolled-encoder-decoder.png	23.0 KB
faces.png	43.8 KB
convolutional-neural-networks-2.jpg	61.1 KB
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rnn.png	159.4 KB
karpathy-network.png	227.1 KB
screen-shot-2018-06-12-at-5.07.10-pm.png	263.6 KB
screen-shot-2017-01-26-at-2.51.02-pm.png	309.8 KB
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examples.jpg	480.4 KB
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magic-pdb.png	70.3 KB
nbconvert-example.png	75.1 KB
notebook-download.png	81.5 KB
magic-matplotlib.png	92.9 KB
notebook-json.png	97.6 KB
new-notebook.png	104.2 KB
notebook-server.png	105.8 KB
conda-tab.png	112.6 KB
server-shutdown.png	159.2 KB
magic-timeit.png	161.1 KB
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roc.png	80.9 KB
sample-confusion-matrix.png	133.7 KB
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sensitivity-specificity.png	158.9 KB
precision-recall.png	160.5 KB
new-confusion-matrix.png	190.6 KB
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cat-2.jpeg	236.8 KB
confusion-matrix.png	318.4 KB
threshold.png	479.5 KB
cat-3.png	589.7 KB
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lesions.png	1.6 MB
skin-disease-classes.png	1.7 MB
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bootstrap.min.js	51.0 KB
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accuracy.png	183.6 KB
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command+palette.mp4	173.2 KB
notebook+interface.mp4	220.6 KB
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command+palette.mp4	173.2 KB
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svhn-examples.png	174.0 KB
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input-to-output-2.mp4	176.2 KB
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generated-mnist.png	354.3 KB
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carnd.jpg	5.6 MB
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