DesireCourse Net Udemy Master Deep Learning with TensorFlow in Python

DesireCourse Net Udemy Master Deep Learning with TensorFlow in Python

Name	[DesireCourse.Net] Udemy - Master Deep Learning with TensorFlow in Python	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	1.5 GB
Total Files	296
Hash	B32FA4DC9596273110ECC8A0055A9502488D916B

/1. Welcome! Course introduction/
1. Meet your instructors and why you should study machine learning.mp4	110.9 MB
1. Meet your instructors and why you should study machine learning.vtt	9.0 KB
2. What does the course cover.mp4	17.2 MB
2. What does the course cover.vtt	5.6 KB
3. What does the course cover - Quiz.html	0.2 KB
/10. Gradient descent and learning rates/
1. Stochastic gradient descent.mp4	9.8 MB
1. Stochastic gradient descent.vtt	4.3 KB
2. Gradient descent pitfalls.mp4	4.5 MB
2. Gradient descent pitfalls.vtt	2.6 KB
3. Momentum.mp4	6.4 MB
3. Momentum.vtt	3.2 KB
4. Learning rate schedules.mp4	10.8 MB
4. Learning rate schedules.vtt	5.4 KB
5. Learning rate schedules. A picture.mp4	3.3 MB
5. Learning rate schedules. A picture.vtt	1.9 KB
6. Adaptive learning rate schedules.mp4	9.3 MB
6. Adaptive learning rate schedules.vtt	4.7 KB
7. Adaptive moment estimation.mp4	8.2 MB
7. Adaptive moment estimation.vtt	3.0 KB
/11. Preprocessing/
1. Preprocessing introduction.mp4	8.8 MB
1. Preprocessing introduction.vtt	3.5 KB
2. Basic preprocessing.mp4	3.8 MB
2. Basic preprocessing.vtt	1.5 KB
3. Standardization.mp4	8.7 MB
3. Standardization.vtt	5.4 KB
4. Dealing with categorical data.mp4	6.4 MB
4. Dealing with categorical data.vtt	2.5 KB
5. One-hot and binary encoding.mp4	6.5 MB
5. One-hot and binary encoding.vtt	4.3 KB
/12. The MNIST example/
1. The dataset.mp4	7.7 MB
1. The dataset.vtt	3.1 KB
10. MNIST - exercises.html	2.3 KB
10.1 MNIST_Exercises_All.html	0.1 KB
11. MNIST - solutions.html	2.3 KB
11.1 MNIST_Depth_Solution.html	0.2 KB
11.10 MNIST_Learning_rate_Part_1_Solution.html	0.2 KB
11.11 TensorFlow_MNIST_Activation_functions_Part_1_Solution.html	0.2 KB
11.2 MNIST_take_note_of_time_Solution.html	0.2 KB
11.3 Width_and_Depth_Solution.html	0.2 KB
11.4 MNIST_Learning_rate_Part_2_Solution.html	0.2 KB
11.5 MNIST_around_98_percent_accuracy_solution.html	0.2 KB
11.6 MNIST_Batch_size_Part_2_Solution.html	0.2 KB
11.7 MNIST_Width_Solution.html	0.2 KB
11.8 MNIST_Batch_size_Part_1_Solution.html	0.2 KB
11.9 MNIST_Activation_functions_Part_2_Solution.html	0.2 KB
2. How to tackle the MNIST.mp4	7.7 MB
2. How to tackle the MNIST.vtt	3.3 KB
3. Importing the relevant packages.mp4	5.7 MB
3. Importing the relevant packages.vtt	2.0 KB
3.1 TensorFlow_MNIST_with_comments_Part_1.html	0.2 KB
4. Outlining the model.mp4	19.3 MB
4. Outlining the model.vtt	8.0 KB
4.1 TensorFlow_MNIST_with_comments_Part_2.html	0.2 KB
5. Declaring the loss and the optimization algorithm.mp4	7.5 MB
5. Declaring the loss and the optimization algorithm.vtt	3.2 KB
5.1 TensorFlow_MNIST_with_comments_Part_3.html	0.2 KB
6. Accuracy of prediction.mp4	13.0 MB
6. Accuracy of prediction.vtt	4.7 KB
6.1 TensorFlow_MNIST_with_comments_Part_4.html	0.2 KB
7. Batching and early stopping.mp4	4.8 MB
7. Batching and early stopping.vtt	2.5 KB
7.1 TensorFlow_MNIST_with_comments_Part_5.html	0.2 KB
8. Learning.mp4	16.7 MB
8. Learning.vtt	9.1 KB
8.1 TensorFlow_MNIST_with_comments_Part_6.html	0.2 KB
9. Discuss the results and test.mp4	23.0 MB
9. Discuss the results and test.vtt	7.4 KB
9.1 TensorFlow_MNIST_with_comments.html	0.2 KB
/13. Business case/
1. Exploring the dataset and identifying predictors.mp4	24.4 MB
1. Exploring the dataset and identifying predictors.vtt	9.7 KB
1.1 Audiobooks_data.csv.csv	727.8 KB
10. Testing the model.mp4	4.5 MB
10. Testing the model.vtt	2.4 KB
11. A comment on the homework.mp4	13.6 MB
11. A comment on the homework.vtt	4.7 KB
11.1 Homework exercise.html	0.1 KB
12. Final exercise.html	0.4 KB
12.1 Homework exercise.html	0.1 KB
2. Outlining the business case solution.mp4	4.0 MB
2. Outlining the business case solution.vtt	2.3 KB
3. Balancing the dataset.mp4	14.5 MB
3. Balancing the dataset.vtt	4.0 KB
4. Preprocessing the data.mp4	36.0 MB
4. Preprocessing the data.vtt	12.0 KB
4.1 Preprocessing.html	0.1 KB
5. Preprocessing exercise.html	0.4 KB
5.1 Preprocessing exercise.html	0.1 KB
6. Create a class for batching.mp4	29.0 MB
6. Create a class for batching.vtt	7.0 KB
6.1 Class.html	0.1 KB
7. Outlining the model.mp4	20.4 MB
7. Outlining the model.vtt	6.2 KB
7.1 Outlining the model.html	0.1 KB
8. Optimizing the algorithm.mp4	12.8 MB
8. Optimizing the algorithm.vtt	5.9 KB
8.1 Optimizing the algorithm.html	0.1 KB
9. Interpreting the result.mp4	5.6 MB
9. Interpreting the result.vtt	2.6 KB
9.1 Interpreting the result.html	0.1 KB
/14. Appendix Linear Algebra Fundamentals/
1. What is a Matrix.mp4	35.2 MB
1. What is a Matrix.vtt	3.9 KB
10. Dot Product of Matrices.mp4	51.8 MB
10. Dot Product of Matrices.vtt	8.4 KB
10.1 Dot Product of Matrices Python Notebook.html	0.2 KB
11. Why is Linear Algebra Useful.mp4	151.3 MB
11. Why is Linear Algebra Useful.vtt	10.6 KB
2. Scalars and Vectors.mp4	35.5 MB
2. Scalars and Vectors.vtt	3.4 KB
3. Linear Algebra and Geometry.mp4	52.2 MB
3. Linear Algebra and Geometry.vtt	3.6 KB
4. Scalars, Vectors and Matrices in Python.mp4	28.0 MB
4. Scalars, Vectors and Matrices in Python.vtt	5.4 KB
4.1 Scalars, Vectors and Matrices Python Notebook.html	0.2 KB
5. Tensors.mp4	23.6 MB
5. Tensors.vtt	3.2 KB
5.1 Tensors Notebook.html	0.1 KB
6. Addition and Subtraction of Matrices.mp4	34.2 MB
6. Addition and Subtraction of Matrices.vtt	3.6 KB
6.1 Addition and Subtraction Python Notebook.html	0.2 KB
7. Errors when Adding Matrices.mp4	11.7 MB
7. Errors when Adding Matrices.vtt	2.3 KB
7.1 Errors when Adding Matrices Python Notebook.html	0.2 KB
8. Transpose of a Matrix.mp4	39.9 MB
8. Transpose of a Matrix.vtt	4.8 KB
8.1 Transpose of a Matrix Python Notebook.html	0.2 KB
9. Dot Product of Vectors.mp4	25.2 MB
9. Dot Product of Vectors.vtt	3.8 KB
9.1 Dot Product Python Notebook.html	0.2 KB
/15. Conclusion/
1. See how much you have learned.mp4	14.6 MB
1. See how much you have learned.vtt	4.7 KB
2. What’s further out there in the machine and deep learning world.mp4	6.6 MB
2. What’s further out there in the machine and deep learning world.vtt	2.3 KB
3. An overview of CNNs.mp4	11.5 MB
3. An overview of CNNs.vtt	5.8 KB
4. How DeepMind uses deep learning.html	1.4 KB
5. An overview of RNNs.mp4	5.1 MB
5. An overview of RNNs.vtt	3.3 KB
6. An overview of non-NN approaches.mp4	8.2 MB
6. An overview of non-NN approaches.vtt	4.7 KB
/16. Bonus lecture/
1. Bonus lecture Next steps.html	2.6 KB
/2. Introduction to neural networks/
1. Introduction to neural networks.mp4	14.2 MB
1. Introduction to neural networks.vtt	5.3 KB
1.1 Course Notes - Section 2.pdf.pdf	949.9 KB
10. The linear model. Multiple inputs.mp4	7.9 MB
10. The linear model. Multiple inputs.vtt	2.8 KB
10.1 Course Notes - Section 2.pdf.pdf	949.9 KB
11. The linear model. Multiple inputs - Quiz.html	0.2 KB
12. The linear model. Multiple inputs and multiple outputs.mp4	40.1 MB
12. The linear model. Multiple inputs and multiple outputs.vtt	4.9 KB
12.1 Course Notes - Section 2.pdf.pdf	949.9 KB
13. The linear model. Multiple inputs and multiple outputs - Quiz.html	0.2 KB
14. Graphical representation.mp4	6.7 MB
14. Graphical representation.vtt	2.4 KB
14.1 Course Notes - Section 2.pdf.pdf	949.9 KB
15. Graphical representation - Quiz.html	0.2 KB
16. The objective function.mp4	6.0 MB
16. The objective function.vtt	1.9 KB
16.1 Course Notes - Section 2.pdf.pdf	949.9 KB
17. The objective function - Quiz.html	0.2 KB
18. L2-norm loss.mp4	7.6 MB
18. L2-norm loss.vtt	2.5 KB
18.1 Course Notes - Section 2.pdf.pdf	949.9 KB
19. L2-norm loss - Quiz.html	0.2 KB
2. Introduction to neural networks - Quiz.html	0.2 KB
20. Cross-entropy loss.mp4	11.9 MB
20. Cross-entropy loss.vtt	4.7 KB
20.1 Course Notes - Section 2.pdf.pdf	949.9 KB
21. Cross-entropy loss - Quiz.html	0.2 KB
22. One parameter gradient descent.mp4	18.6 MB
22. One parameter gradient descent.vtt	7.6 KB
22.1 GD-function-example.xlsx.xlsx	43.4 KB
22.2 Course Notes - Section 2.pdf.pdf	949.9 KB
23. One parameter gradient descent - Quiz.html	0.2 KB
24. N-parameter gradient descent.mp4	41.4 MB
24. N-parameter gradient descent.vtt	6.8 KB
24.1 Course Notes - Section 2.pdf.pdf	949.9 KB
25. N-parameter gradient descent - Quiz.html	0.2 KB
3. Training the model.mp4	9.2 MB
3. Training the model.vtt	3.9 KB
3.1 Course Notes - Section 2.pdf.pdf	949.9 KB
4. Training the model - Quiz.html	0.2 KB
5. Types of machine learning.mp4	12.8 MB
5. Types of machine learning.vtt	4.8 KB
5.1 Course Notes - Section 2.pdf.pdf	949.9 KB
6. Types of machine learning - Quiz.html	0.2 KB
7. The linear model.mp4	9.6 MB
7. The linear model.vtt	3.6 KB
7.1 Course Notes - Section 2.pdf.pdf	949.9 KB
8. The linear model - Quiz.html	0.2 KB
9. Need Help with Linear Algebra.html	0.8 KB
/3. Setting up the working environment/
1. Setting up the environment - An introduction - Do not skip, please!.mp4	2.8 MB
1. Setting up the environment - An introduction - Do not skip, please!.vtt	1.1 KB
10. Installing packages - exercise.html	0.2 KB
11. Installing packages - solution.html	0.3 KB
2. Why Python and why Jupyter.mp4	14.3 MB
2. Why Python and why Jupyter.vtt	5.7 KB
3. Why Python and why Jupyter - Quiz.html	0.2 KB
4. Installing Anaconda.mp4	9.8 MB
4. Installing Anaconda.vtt	4.2 KB
5. The Jupyter dashboard - part 1.mp4	5.9 MB
5. The Jupyter dashboard - part 1.vtt	2.8 KB
6. The Jupyter dashboard - part 2.mp4	11.4 MB
6. The Jupyter dashboard - part 2.vtt	6.1 KB
7. Jupyter Shortcuts.html	0.3 KB
7.1 Shortcuts for Jupyter.pdf.pdf	634.0 KB
8. The Jupyter dashboard - Quiz.html	0.2 KB
9. Installing the TensorFlow package.mp4	5.1 MB
9. Installing the TensorFlow package.vtt	2.9 KB
/4. Minimal example - your first machine learning algorithm/
1. Minimal example - part 1.mp4	6.9 MB
1. Minimal example - part 1.vtt	4.0 KB
1.1 Minimal example Part 1.html	0.1 KB
2. Minimal example - part 2.mp4	11.2 MB
2. Minimal example - part 2.vtt	6.1 KB
2.1 Minimal example - part 2.html	0.1 KB
3. Minimal example - part 3.mp4	10.2 MB
3. Minimal example - part 3.vtt	4.0 KB
3.1 Minimal example - part 3.html	0.1 KB
4. Minimal example - part 4.mp4	21.8 MB
4. Minimal example - part 4.vtt	9.7 KB
4.1 Minimal example - part 4.html	0.1 KB
5. Minimal example - Exercises.html	1.6 KB
5.1 Minimal_example_Exercise_2_Solution.html	0.1 KB
5.10 Minimal_example_Exercise_6_Solution.html	0.1 KB
5.2 Minimal_example_Exercise_3.d. Solution.html	0.2 KB
5.3 Minimal_example_Exercise_4_Solution.html	0.1 KB
5.4 Minimal_example_Exercise_3.b. Solution.html	0.2 KB
5.5 Minimal_example_All_Exercises.html	0.1 KB
5.6 Minimal_example_Exercise_1_Solution.html	0.1 KB
5.7 Minimal_example_Exercise_3.c. Solution.html	0.2 KB
5.8 Minimal_example_Exercise_5_Solution.html	0.1 KB
5.9 Minimal_example_Exercise_3.a. Solution.html	0.2 KB
/5. TensorFlow - An introduction/
1. TensorFlow outline.mp4	15.2 MB
1. TensorFlow outline.vtt	4.7 KB
2. TensorFlow intro.mp4	7.9 MB
2. TensorFlow intro.vtt	2.0 KB
3. Types of file formats in TensorFlow.mp4	6.1 MB
3. Types of file formats in TensorFlow.vtt	3.1 KB
3.1 TensorFlow Minimal example - Part 1.html	0.2 KB
4. Inputs, outputs, targets, weights, biases - model layout.mp4	13.6 MB
4. Inputs, outputs, targets, weights, biases - model layout.vtt	6.6 KB
4.1 TensorFlow Minimal example - Part 2.html	0.2 KB
5. Loss function and gradient descent - introducing optimizers.mp4	10.2 MB
5. Loss function and gradient descent - introducing optimizers.vtt	4.3 KB
5.1 TensorFlow Minimal example - Part 3.html	0.2 KB
6. Model output.mp4	15.0 MB
6. Model output.vtt	7.0 KB
6.1 TensorFlow - Minimal example complete.html	0.2 KB
7. Minimal example - Exercises.html	1.7 KB
7.1 TensorFlow_Minimal_Example_Exercise_1_Solution.html	0.2 KB
7.2 TensorFlow_Minimal_Example_Exercise_2_3_Solution.html	0.2 KB
7.3 TensorFlow_Minimal_Example_Exercise_2_1_Solution.html	0.2 KB
7.4 TensorFlow_Minimal_Example_All_Exercises.html	0.2 KB
7.5 TensorFlow_Minimal_Example_Exercise_3_Solution.html	0.2 KB
7.6 TensorFlow_Minimal_Example_Exercise_2_2_Solution.html	0.2 KB
7.7 TensorFlow_Minimal_Example_Exercise_4_Solution.html	0.2 KB
7.8 TensorFlow_Minimal_Example_Exercise_2_4_Solution.html	0.2 KB
/6. Going deeper Introduction to deep neural networks/
1. Layers.mp4	5.0 MB
1. Layers.vtt	2.2 KB
1.1 Course Notes - Section 6.pdf.pdf	958.9 KB
2. What is a deep net.mp4	7.0 MB
2. What is a deep net.vtt	2.9 KB
2.1 Course Notes - Section 6.pdf.pdf	958.9 KB
3. Understanding deep nets in depth.mp4	14.1 MB
3. Understanding deep nets in depth.vtt	6.0 KB
4. Why do we need non-linearities.mp4	9.4 MB
4. Why do we need non-linearities.vtt	3.4 KB
5. Activation functions.mp4	9.2 MB
5. Activation functions.vtt	4.6 KB
6. Softmax activation.mp4	7.7 MB
6. Softmax activation.vtt	7.7 MB
7. Backpropagation.mp4	11.6 MB
7. Backpropagation.vtt	6.8 MB
8. Backpropagation - visual representation.mp4	7.2 MB
8. Backpropagation - visual representation.vtt	3.6 KB
/7. Backpropagation. A peek into the Mathematics of Optimization/
1. Backpropagation. A peek into the Mathematics of Optimization.html	0.5 KB
1.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf	186.7 KB
/8. Overfitting/
1. Underfitting and overfitting.mp4	11.6 MB
1. Underfitting and overfitting.vtt	5.1 KB
2. Underfitting and overfitting - classification.mp4	7.1 MB
2. Underfitting and overfitting - classification.vtt	2.4 KB
3. Training and validation.mp4	9.7 MB
3. Training and validation.vtt	4.3 KB
4. Training, validation, and test.mp4	7.8 MB
4. Training, validation, and test.vtt	3.2 KB
5. N-fold cross validation.mp4	7.3 MB
5. N-fold cross validation.vtt	3.8 KB
6. Early stopping.mp4	9.9 MB
6. Early stopping.vtt	6.2 KB
/9. Initialization/
1. Initialization - Introduction.mp4	8.4 MB
1. Initialization - Introduction.vtt	3.2 KB
2. Types of simple initializations.mp4	5.9 MB
2. Types of simple initializations.vtt	3.3 KB
3. Xavier initialization.mp4	6.1 MB
3. Xavier initialization.vtt	3.3 KB
/
[CourseClub.Me].url	0.0 KB
[DesireCourse.Net].url	0.1 KB
Total files 296