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FreeCoursesOnline Me UDACITY Deep Learning Nanodegree Program FCO

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[FreeCoursesOnline.Me] [UDACITY] Deep Learning Nanodegree Program - [FCO]

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/.../Lesson 01_Welcome to Deep Learning/

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/.../Lesson 02_Applying Deep Learning/

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/.../Lesson 03_Anaconda/

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/.../Lesson 04_Jupyter Notebooks/

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/.../Lesson 05_Matrix Math and NumPy Refresher/

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/.../Lesson 01_Introduction to Neural Networks/

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/.../Lesson 02_Implementing Gradient Descent/

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/.../Lesson 03_Training Neural Networks/

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/.../Lesson 04_GPU Workspaces Demo/

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/.../Lesson 05_Project Predicting Bike Sharing Data/

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/.../Lesson 06_Sentiment Analysis/

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/.../Lesson 07_Keras/

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/.../Lesson 08_TensorFlow/

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/.../Lesson 01_Cloud Computing/

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/.../Lesson 02_Convolutional Neural Networks/

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/.../Lesson 03_CNNs in TensorFlow/

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/.../Lesson 04_Weight Initialization/

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/.../Lesson 05_Autoencoders/

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/.../Lesson 06_Transfer Learning in TensorFlow/

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/.../Lesson 07_CNN Project Dog Breed Classifier/

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/.../Lesson 08_Deep Learning for Cancer Detection with Sebastian Thrun/

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/.../Lesson 01_Recurrent Neural Networks/

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/.../Lesson 02_Long Short-Term Memory Networks (LSTM)/

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/.../Lesson 03_Implementation of RNN and LSTM/

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/.../Lesson 04_Hyperparameters/

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/.../Lesson 05_Embeddings and Word2vec/

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/.../Lesson 06_Sentiment Prediction RNN/

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/.../Lesson 07_Generate TV Scripts/

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/.../Lesson 01_Generative Adversarial Networks/

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/.../Lesson 02_Deep Convolutional GANs/

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/.../Lesson 03_Generate Faces/

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/.../Lesson 04_Semi-Supervised Learning/

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/.../Lesson 01_Introduction to RL/

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/.../Lesson 02_The RL Framework The Problem/

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/.../Lesson 03_The RL Framework The Solution/

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/.../Lesson 04_Dynamic Programming/

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/.../Lesson 05_Monte Carlo Methods/

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/.../Lesson 06_Temporal-Difference Methods/

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/.../Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/

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/.../Lesson 01_RL in Continuous Spaces/

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/.../Lesson 02_Deep Q-Learning/

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/.../Lesson 03_Policy-Based Methods/

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/.../Lesson 04_Actor-Critic Methods/

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/.../Lesson 05_Teach a Quadcopter How to Fly/

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rubric.json

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/.../Lesson 01_Enroll in your next Nanodegree program/

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/.../Lesson 01_Evaluation Metrics/

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/.../Lesson 02_Regression/

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/.../Lesson 01_MiniFlow/

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/Part 01-Module 01-Lesson 01_Welcome to Deep Learning/

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/Part 01-Module 01-Lesson 02_Applying Deep Learning/

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/Part 01-Module 01-Lesson 03_Anaconda/

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/Part 01-Module 01-Lesson 04_Jupyter Notebooks/

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/Part 01-Module 01-Lesson 05_Matrix Math and NumPy Refresher/

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/Part 02-Module 01-Lesson 01_Introduction to Neural Networks/

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22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt

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23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt

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23. Logistic Regression.html

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24. Gradient Descent.html

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25. Logistic Regression Algorithm.html

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26. Pre-Lab Gradient Descent.html

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27. Notebook Gradient Descent.html

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28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt

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28. Perceptron vs Gradient Descent.html

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30. Non-Linear Data-F7ZiE8PQiSc.en.vtt

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30. Non-linear Data.html

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31. Non-Linear Models.html

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32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt

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32. Combinando modelos-Boy3zHVrWB4.en.vtt

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32. Neural Network Architecture.html

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33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt

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33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt

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33. Feedforward.html

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34. Chain Rule-YAhIBOnbt54.en.vtt

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34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt

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35. Pre-Lab Analyzing Student Data.html

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36. Notebook Analyzing Student Data.html

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37. Outro.html

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index.html

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/Part 02-Module 01-Lesson 02_Implementing Gradient Descent/

01. Mean Squared Error Function.html

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02. Gradient Descent-29PmNG7fuuM.en.vtt

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02. Gradient Descent.html

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03. Gradient Descent The Math.html

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04. Gradient Descent The Code.html

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05. Implementing Gradient Descent.html

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06. Multilayer Perceptrons.html

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08. Implementing Backpropagation.html

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09. Further Reading.html

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index.html

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/Part 02-Module 01-Lesson 03_Training Neural Networks/

01. Instructor.html

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02. Training Optimization.html

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03. Testing.html

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04. Overfitting and Underfitting.html

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05. Early Stopping.html

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06. DL 53 Q Regularization-KxROxcRsHL8.en.vtt

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06. Regularization.html

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08. Dropout.html

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11. Vanishing Gradient.html

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12. Other Activation Functions.html

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13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt

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14. Learning Rate Decay.html

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15. Momentum.html

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16. Error Functions Around the World-34AAcTECu2A.en.vtt

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index.html

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/Part 02-Module 01-Lesson 04_GPU Workspaces Demo/

01. Introduction to GPU Workspaces.html

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02. Workspace Playground.html

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index.html

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/Part 02-Module 01-Lesson 05_Project Predicting Bike Sharing Data/

01. Introduction to the Project-dOwEDeJp8yw.en.vtt

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02. Project Workspace.html

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index.html

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Project Description - Your first neural network.html

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Project Rubric - Your first neural network.html

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/Part 02-Module 01-Lesson 06_Sentiment Analysis/

01. Introducing Andrew Trask.html

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02. Meet Andrew.html

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03. Materials.html

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04. The Notebooks.html

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06. Mini Project 1.html

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09. Mini Project 2.html

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11. Building a Neural Network-aM2k7RTjjJI.en.vtt

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14. Understanding Neural Noise.html

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15. Mini Project 4.html

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16. Understanding Inefficiencies in our Network-4MuS-6ATxCU.en.vtt

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16. Understanding Inefficiencies in our Network.html

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17. Mini Project 5.html

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19. Further Noise Reduction.html

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20. Mini Project 6.html

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23. Andrew Trask - Outro-nIF0GLOQglQ.en-US.vtt

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23. Conclusion.html

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/Part 02-Module 01-Lesson 07_Keras/

01. Intro.html

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02. Keras.html

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03. Pre-Lab Student Admissions in Keras.html

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index.html

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/Part 02-Module 01-Lesson 08_TensorFlow/

01. Intro.html

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02. Installing TensorFlow.html

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03. Hello, Tensor World!.html

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04. Quiz TensorFlow Linear Function.html

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05. Quiz TensorFlow Softmax.html

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06. Quiz TensorFlow Cross Entropy.html

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07. Quiz Mini-batch.html

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08. Epochs.html

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09. Pre-Lab NotMNIST in TensorFlow.html

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10. Lab NotMNIST in TensorFlow.html

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11. Two-layer Neural Network.html

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12. Quiz TensorFlow ReLUs.html

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13. Deep Neural Network in TensorFlow.html

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14. Save and Restore TensorFlow Models.html

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17. Outro.html

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index.html

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/Part 03-Module 01-Lesson 01_Cloud Computing/

01. Overview.html

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02. Create an AWS Account.html

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03. Get Access to GPU Instances.html

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04. Apply Credits.html

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06. Login to the Instance.html

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index.html

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/Part 03-Module 01-Lesson 02_Convolutional Neural Networks/

01. Introducing Alexis-38ExGpdyvJI.en.vtt

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01. Introducing Alexis.html

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02. Applications of CNNs-HrYNL_1SV2Y.en.vtt

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07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt

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08. Mini Project Training an MLP on MNIST.html

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10. Convolutional Layers-h5R_JvdUrUI.en.vtt

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10. Convolutional Layers (Part 1).html

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12. Stride and Padding.html

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13. Convolutional Layers in Keras.html

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14. Quiz Dimensionality.html

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15. Pooling Layers.html

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16. Max Pooling Layers in Keras.html

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17. CNNs for Image Classification.html

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18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt

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18. CNNs in Keras Practical Example.html

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19. Mini Project CNNs in Keras.html

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20. Image Augmentation in Keras.html

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20. Image Augmentation in Keras-odStujZq3GY.mp4

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21. Mini Project Image Augmentation in Keras.html

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22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt

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22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4

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23. Visualizing CNNs-mnqS_EhEZVg.en.vtt

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23. Visualizing CNNs (Part 1).html

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24. Visualizing CNNs (Part 2).html

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25. Transfer Learning.html

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25. Transfer Learning-LHG5FltaR6I.en.vtt

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25. Transfer Learning-LHG5FltaR6I.mp4

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26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt

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26. Transfer Learning in Keras.html

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index.html

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/Part 03-Module 01-Lesson 03_CNNs in TensorFlow/

01. Convolutional Layers.html

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02. Quiz Convolutional Layers.html

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03. Solution Convolutional Layers.html

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04. Max Pooling Layers.html

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06. Solution Max Pooling Layers.html

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07. CNNs in TensorFlow.html

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08. CNNs - Additional Resources.html

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index.html

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/Part 03-Module 01-Lesson 04_Weight Initialization/

01. Weight Initialization Intro.html

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02. Ones and Zeros.html

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02. Weight Initialization 1-6vXMYu_TQIA.en.vtt

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02. Weight Initialization 1-6vXMYu_TQIA.mp4

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04. Too Small.html

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06. Additional Material.html

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/Part 03-Module 01-Lesson 05_Autoencoders/

01. Autoencoder Lesson Intro.html

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02. Autoencoders-ar5Iyx68cWc.en.vtt

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02. Autoencoders-ar5Iyx68cWc.mp4

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02. Autoencoders-ar5Iyx68cWc.pt-BR.vtt

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02. Autoencoders.html

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03. A Simple Autoencoder.html

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04. Simple Autoencoder Solution.html

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05. Convolutional Autoencoders-18SZVRaumGs.en.vtt

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index.html

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/Part 03-Module 01-Lesson 06_Transfer Learning in TensorFlow/

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02. Transfer Learning with VGGNet.html

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03. Building VGGNet-615SslQiGvo.en.vtt

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03. VGGNet.html

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05. Data Preparation.html

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index.html

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/Part 03-Module 01-Lesson 07_CNN Project Dog Breed Classifier/

01. CNN Project.html

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02. Dog Breed Workspace.html

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index.html

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Project Description - Dog Breed Classifier.html

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Project Rubric - Dog Breed Classifier.html

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/Part 03-Module 01-Lesson 08_Deep Learning for Cancer Detection with Sebastian Thrun/

01. Introduction-ZCpXvVdIdnY.en.vtt

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01. Intro.html

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02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt

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02. Skin Cancer.html

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09. Training the Neural Network.html

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10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt

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11. Solution Random vs Pre-initialized Weight.html

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12. Validating the Training.html

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12. Validating The Training-Oxm9ofvov3I.mp4

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13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt

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13. Quiz Sensitivity and Specificity.html

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14. Solution Sensitivity and Specificity.html

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15. More on Sensitivity and Specificity.html

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16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt

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16. Quiz Diagnosing Cancer.html

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17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt

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17. Solution Diagnosing Cancer.html

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18. Images-1GdiN5Wc8LA.mp4

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18. Refresh on ROC Curves.html

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18. ROC Curve-2Iw5TiGzJI4.en.vtt

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18. ROC Curve-2Iw5TiGzJI4.mp4

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18. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt

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18. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt

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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt

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19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4

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19. Quiz ROC Curve.html

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20. Solution ROC Curve.html

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20. Solution ROC Curve-sdUUf6RRmXI.en.vtt

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20. Solution ROC Curve-sdUUf6RRmXI.mp4

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21. Comparing our Results with Doctors.html

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21. ROC Curve-fWwe_JlpnlQ.en.vtt

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21. ROC Curve-fWwe_JlpnlQ.mp4

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22. Visualization-aGIGB4Ta3_A.en.vtt

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22. Visualization-aGIGB4Ta3_A.mp4

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22. Visualization-aGIGB4Ta3_A.pt-BR.vtt

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22. Visualization.html

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23. What is the network looking at.html

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23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt

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23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4

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23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt

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24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4

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24. Refresh on Confusion Matrices.html

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25. Confusion Matrix-3rpN-YYlfes.en.vtt

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25. Confusion Matrix-3rpN-YYlfes.mp4

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25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt

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25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt

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25. Confusion Matrix.html

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26. Conclusion.html

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26. Conclusion-WhpE_8sTt-0.en.vtt

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26. Conclusion-WhpE_8sTt-0.mp4

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26. Conclusion-WhpE_8sTt-0.pt-BR.vtt

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26. Conclusion-WhpE_8sTt-0.zh-CN.vtt

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27. Useful Resources.html

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28. Mini Project Introduction.html

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28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt

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28. Mini Project Introduction-Rgf3YVFWl-M.mp4

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28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt

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28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt

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29. Mini Project Dermatologist AI.html

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index.html

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/Part 04-Module 01-Lesson 01_Recurrent Neural Networks/

01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.en.vtt

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.mp4

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.pt-BR.vtt

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01. 00 Luis Introducing Ortal Newtitle121217-oXv7GiC-jrM.zh-CN.vtt

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01. Introducing Ortal .html

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.en.vtt

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.mp4

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.pt-BR.vtt

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02. 01 RNN Intro V6 Final-AIQEqg6F38A.zh-CN.vtt

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02. RNN Introduction.html

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03. 02 RNN History V4 Final-HbxAnYUfRnc.en.vtt

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03. 02 RNN History V4 Final-HbxAnYUfRnc.mp4

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03. RNN History.html

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04. 03 RNN Applications V3 Final-6JbTNARuKII.en.vtt

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04. 03 RNN Applications V3 Final-6JbTNARuKII.mp4

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04. RNN Applications.html

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.en.vtt

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05. 04 RNN FFNN Reminder A V7 Final-_vrp2lZjXf0.mp4

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05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.en.vtt

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05. 05 RNN FFNN Reminder B V6 Final-FfPjaGcZODc.mp4

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05. Feedforward Neural Network-Reminder.html

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.en.vtt

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06. 06 FeedForward A V7 Final-4rCfnWbx8-0.mp4

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06. 07 FeedForward B V3-kTYbTVh1d0k.en.vtt

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06. The Feedforward Process.html

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07. Feedforward Quiz.html

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08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.en.vtt

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08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.mp4

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08. 08 Backpropagation Theory V6 Final-Xlgd8I3TWUg.pt-BR.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.en.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.mp4

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.pt-BR.vtt

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08. 13 Overfitting Intro V4 Final-rmBLnVbFfFY.zh-CN.vtt

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08. Backpropagation- Theory.html

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.en.vtt

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.mp4

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.pt-BR.vtt

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09. 10 Backpropagation Example A V3 Final-3k72z_WaeXg.zh-CN.vtt

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09. Backpropagation - Example (part a).html

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09. Regra da cadeia-YAhIBOnbt54.en.vtt

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09. Regra da cadeia-YAhIBOnbt54.mp4

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09. Regra da cadeia-YAhIBOnbt54.pt-BR.vtt

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09. Regra da cadeia-YAhIBOnbt54.zh-CN.vtt

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.en.vtt

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.mp4

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.pt-BR.vtt

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10. 12 Backpropagation Example B V6 Final-yiSwuMP2UIA.zh-CN.vtt

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10. Backpropagation- Example (part b).html

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11. Backpropagation Quiz.html

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12. 14 RNN A V4 Final-ofbnDxGSUcg.en.vtt

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12. 14 RNN A V4 Final-ofbnDxGSUcg.mp4

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12. 14 RNN A V4 Final-ofbnDxGSUcg.pt-BR.vtt

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12. RNN (part a).html

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13. 16 RNN B V4 Final-wsif3p5t7CI.en.vtt

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13. 16 RNN B V4 Final-wsif3p5t7CI.mp4

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13. RNN (part b).html

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.en.vtt

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14. 17 RNN Unfolded V3 Final-xLIA_PTWXog.mp4

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14. RNN- Unfolded Model.html

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15. Unfolded Model Quiz.html

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.en.vtt

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16. 18 RNN Example V5 Final-MDLk3fhpTx0.mp4

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16. RNN- Example.html

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.en.vtt

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.mp4

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17. 19 RNN BPTT A V6 Final-eE2L3-2wKac.zh-CN.vtt

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17. Backpropagation Through Time (part a).html

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.en.vtt

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.mp4

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18. 20 RNN BPTT B V5 Final-bUU9BEQw0IA.zh-CN.vtt

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18. Backpropagation Through Time (part b).html

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.en.vtt

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.mp4

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.pt-BR.vtt

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19. 21 RNN BPTT C V7 Final-uBy_eIJDD1M.zh-CN.vtt

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19. Backpropagation Through Time (part c).html

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20. BPTT Quiz 1.html

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21. BPTT Quiz 2.html

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22. BPTT Quiz 3.html

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23. Some more math.html

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24. RNN Summary.html

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24. RNN Summary-nXP0oGGRrO8.en.vtt

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24. RNN Summary-nXP0oGGRrO8.mp4

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24. RNN Summary-nXP0oGGRrO8.pt-BR.vtt

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24. RNN Summary-nXP0oGGRrO8.zh-CN.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.en.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.mp4

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.pt-BR.vtt

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25. 23 From RNNs To LSTMs V4 Final-MsqybcWmzGY.zh-CN.vtt

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25. From RNN to LSTM.html

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26. Wrap Up.html

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index.html

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/Part 04-Module 01-Lesson 02_Long Short-Term Memory Networks (LSTM)/

01. Intro to LSTM.html

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02. RNN Vs LSTM-70MgF-IwAr8.en.vtt

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02. RNN Vs LSTM-70MgF-IwAr8.mp4

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02. RNN Vs LSTM-70MgF-IwAr8.pt-BR.vtt

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02. RNN Vs LSTM-70MgF-IwAr8.zh-CN.vtt

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02. RNN vs LSTM.html

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03. Basics of LSTM.html

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03. LSTM Basics-gjb68a4XsqE.en.vtt

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03. LSTM Basics-gjb68a4XsqE.mp4

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03. LSTM Basics-gjb68a4XsqE.pt-BR.vtt

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03. LSTM Basics-gjb68a4XsqE.zh-CN.vtt

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04. Architecture of LSTM.html

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04. LSTM Architecture-ycwthhdx8ws.en.vtt

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04. LSTM Architecture-ycwthhdx8ws.mp4

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04. LSTM Architecture-ycwthhdx8ws.pt-BR.vtt

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04. LSTM Architecture-ycwthhdx8ws.zh-CN.vtt

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05. Learn Gate-aVHVI7ovbHY.en.vtt

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05. Learn Gate-aVHVI7ovbHY.mp4

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05. Learn Gate-aVHVI7ovbHY.pt-BR.vtt

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05. Learn Gate-aVHVI7ovbHY.zh-CN.vtt

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05. The Learn Gate.html

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06. Forget Gate-iWxpfxLUPSU.en.vtt

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06. Forget Gate-iWxpfxLUPSU.mp4

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06. Forget Gate-iWxpfxLUPSU.pt-BR.vtt

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06. Forget Gate-iWxpfxLUPSU.zh-CN.vtt

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06. The Forget Gate.html

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07. Remember Gate-0qlm86HaXuU.en.vtt

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07. Remember Gate-0qlm86HaXuU.mp4

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07. Remember Gate-0qlm86HaXuU.pt-BR.vtt

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07. Remember Gate-0qlm86HaXuU.zh-CN.vtt

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07. The Remember Gate.html

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08. LSTM 7 Use Gate-5Ifolm1jTdY.en.vtt

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08. LSTM 7 Use Gate-5Ifolm1jTdY.mp4

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08. LSTM 7 Use Gate-5Ifolm1jTdY.zh-CN.vtt

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08. The Use Gate.html

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09. Putting it All Together.html

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09. Putting It All Together-IF8FlKW-Zo0.en.vtt

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09. Putting It All Together-IF8FlKW-Zo0.mp4

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09. Putting It All Together-IF8FlKW-Zo0.pt-BR.vtt

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09. Putting It All Together-IF8FlKW-Zo0.zh-CN.vtt

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10. Quiz.html

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11. Other architectures.html

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11. Other Architectures-MsxFDuYlTuQ.en.vtt

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11. Other Architectures-MsxFDuYlTuQ.mp4

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11. Other Architectures-MsxFDuYlTuQ.pt-BR.vtt

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11. Other Architectures-MsxFDuYlTuQ.zh-CN.vtt

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12. Outro LSTM.html

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index.html

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/Part 04-Module 01-Lesson 03_Implementation of RNN and LSTM/

01. Intro.html

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02. Character-Wise RNN-dXl3eWCGLdU.en.vtt

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02. Character-Wise RNN-dXl3eWCGLdU.mp4

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02. Character-Wise RNN-dXl3eWCGLdU.pt-BR.vtt

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02. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt

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02. Character-wise RNNs.html

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03. Sequence Batching.html

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03. Sequence-Batching-Z4OiyU0Cldg.en.vtt

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03. Sequence-Batching-Z4OiyU0Cldg.mp4

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03. Sequence-Batching-Z4OiyU0Cldg.pt-BR.vtt

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03. Sequence-Batching-Z4OiyU0Cldg.zh-CN.vtt

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04. Character-wise RNN Notebook.html

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05. Implementing a Character-wise RNN.html

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.en.vtt

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.pt-BR.vtt

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05. Implementing a Character-wise RNN-KPCMn_jg2oY.zh-CN.vtt

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06. Batching Data Solution.html

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06. Batching Data Solution-o3nBxHJLQcc.en.vtt

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06. Batching Data Solution-o3nBxHJLQcc.mp4

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07. LSTM Cell-ajC-5uWB8S4.en.vtt

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07. LSTM Cell-ajC-5uWB8S4.mp4

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07. LSTM Cell-ajC-5uWB8S4.pt-BR.vtt

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07. LSTM Cell-ajC-5uWB8S4.zh-CN.vtt

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07. LSTM Cell.html

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08. LSTM Cell Solution.html

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08. LSTM Cell Solution-X4uA0dq_4jA.en.vtt

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08. LSTM Cell Solution-X4uA0dq_4jA.mp4

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09. RNN Output.html

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09. RNN Output-RkanDkcrTxs.mp4

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09. RNN Output-RkanDkcrTxs.pt-BR.vtt

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10. Network Loss.html

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10. Network Loss-itu-uNK4brc.en.vtt

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10. Network Loss-itu-uNK4brc.mp4

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10. Network Loss-itu-uNK4brc.pt-BR.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.en.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.mp4

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11. Output And Loss Solutions-CT8hcU7FmGc.pt-BR.vtt

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11. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt

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11. Output and Loss Solutions.html

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12. Build the Network.html

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index.html

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/Part 04-Module 01-Lesson 04_Hyperparameters/

01. Introducing Jay.html

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02. Introduction.html

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03. Learning Rate-HLMjeDez7ps.en.vtt

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03. Learning Rate.html

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05. Minibatch Size-GrrO1NFxaW8.en.vtt

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06. Number Of Iterations-TTdHpSb4DV8.en.vtt

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06. Number of Training Iterations Epochs.html

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08. RNN Hyperparameters.html

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09. RNN Hyperparameters.html

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10. Sources & References.html

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index.html

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/Part 04-Module 01-Lesson 05_Embeddings and Word2vec/

01. Embeddings Intro.html

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02. Implementing Word2Vec-7M431_f9HgE.en.vtt

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03. Subsampling Solution.html

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04. Making Batches.html

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06. Building The Network-fhSb5c6UX6M.en.vtt

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06. Building the Network.html

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index.html

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/Part 04-Module 01-Lesson 06_Sentiment Prediction RNN/

01. Intro.html

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02. Sentiment Prediction-uGN3rZJRiMY.en.vtt

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02. Sentiment Prediction-uGN3rZJRiMY.mp4

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03. Data Preprocessing-h4-LwZU9_k8.en.vtt

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04. Creating Testing Sets-BRBbrNLz1ho.en.vtt

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05. Building The RNN 1-XTD6slf64fM.en.vtt

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05. Building the RNN.html

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07. Sentiment RNN 2-V9YGGjmoHS0.en.vtt

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07. Solutions.html

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index.html

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/Part 04-Module 01-Lesson 07_Generate TV Scripts/

01. Introduction.html

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02. TV Script Workspace.html

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index.html

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Project Description - Generate TV Scripts.html

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Project Rubric - Generate TV Scripts.html

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/Part 05-Module 01-Lesson 01_Generative Adversarial Networks/

01. GANs Intro-F7XgI6TmaGI.en-US.vtt

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01. Introducing Ian Goodfellow.html

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02. Cool Things To Do With GANs-bo-ToTdhgew.en.vtt

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02. What can you do with GANs.html

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03. How GANs work.html

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04. Games and Equilibria.html

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05. GANs Architecture -gaEs7ccZv_Q.en.vtt

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05. Practical tips and tricks for training GANs.html

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06. Build a GAN.html

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07. Get started with a GAN.html

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08. Generator Network-btHVXnICmzQ.en.vtt

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08. Generator Network.html

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09. Discriminator Network.html

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13. Training Losses.html

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14. Training Optimizers.html

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15. Training Losses and Optimizers Solution.html

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16. A Trained GAN.html

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17. Doing More With Your GAN.html

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index.html

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/Part 05-Module 01-Lesson 02_Deep Convolutional GANs/

01. Deep Convolutional GANs.html

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02. DCGAN Architecture.html

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02. Deconvolution-sX_AxtB6CHI.en.vtt

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03. Batch Normalization.html

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04. DCGAN Implementation.html

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05. DCGAN and the Generator.html

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06. Generator Solution.html

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09. Building and Training the Network.html

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10. Hyperparameter Solutions.html

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index.html

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/Part 05-Module 01-Lesson 03_Generate Faces/

01. Last Project - Congrats-UUqU8SYBZ9Q.en.vtt

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01. Last Project - Congrats-UUqU8SYBZ9Q.mp4

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01. One Project Away!.html

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02. Project Introduction.html

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03. Face Generation Workspace.html

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index.html

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Project Description - Generate Faces.html

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/Part 05-Module 01-Lesson 04_Semi-Supervised Learning/

01. Semi-supervised Learning.html

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02. Semi-Supervised Classification with GANs.html

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03. Introducing Semi-Supervised Learning.html

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04. Data Prep.html

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05. Building The Generator And Discriminator.html

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06. Model Loss Exercise.html

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08. Training The Network.html

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/Part 06-Module 01-Lesson 01_Introduction to RL/

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/Part 06-Module 01-Lesson 02_The RL Framework The Problem/

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/Part 06-Module 01-Lesson 03_The RL Framework The Solution/

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/Part 06-Module 01-Lesson 04_Dynamic Programming/

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01. Introduction.html

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/Part 06-Module 01-Lesson 05_Monte Carlo Methods/

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/Part 06-Module 01-Lesson 06_Temporal-Difference Methods/

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/Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/

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/Part 06-Module 02-Lesson 01_RL in Continuous Spaces/

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12. Kernel Functions.html

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13. Non-Linear Function Approximation.html

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14. Summary.html

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14. Summary-MTEBk43oByU.mp4

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index.html

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/Part 06-Module 02-Lesson 02_Deep Q-Learning/

01. Intro to Deep Q-Learning.html

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt

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01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4

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02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt

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02. Neural Nets as Value Functions.html

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03. Monte Carlo Learning.html

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04. Temporal Difference Learning.html

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05. Q-Learning-AI5gLgYMSq8.en.vtt

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05. Q-Learning.html

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06. Deep Q Network-GgtR_d1OB-M.en.vtt

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06. Deep Q Network.html

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07. Experience Replay.html

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07. Experience Replay-wX_-SZG-YMQ.mp4

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08. Fixed Q Targets.html

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08. Fixed Q Targets-SWpyiEezfp4.mp4

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09. Deep Q-Learning Algorithm.html

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09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4

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10. DQN Improvements.html

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10. DQN Improvements-Zfdbp93A2GU.mp4

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11. Implementing Deep Q-Learning.html

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12. TensorFlow Implementation.html

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13. Wrap Up.html

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13. Wrap Up-x6JggcDTcys.mp4

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index.html

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/Part 06-Module 02-Lesson 03_Policy-Based Methods/

01. M2L3 01 V1-YOSREyp04HA.en.vtt

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01. M2L3 01 V1-YOSREyp04HA.mp4

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01. Policy-Based Methods.html

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02. M2L3 02 V2-ToS8vXGdODE.en.vtt

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02. M2L3 02 V2-ToS8vXGdODE.mp4

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02. Why Policy-Based Methods.html

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03. M2L3 03 V2-TePX-0Bs23E.mp4

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03. Policy Function Approximation.html

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04. M2L3 04 V1-QicxmyE5vTo.mp4

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04. Stochastic Policy Search.html

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05. Policy Gradients.html

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06. Monte Carlo Policy Gradients.html

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07. Constrained Policy Gradients.html

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07. M2L3 07 V2-ZBLLGIN1EfU.mp4

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08. Recap.html

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index.html

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/Part 06-Module 02-Lesson 04_Actor-Critic Methods/

01. Actor-Critic Methods.html

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01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt

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01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4

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02. A Better Score Function.html

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4

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02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4

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03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt

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03. Two Function Approximators.html

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4

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04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt

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04. The Actor and The Critic.html

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05. Advantage Function.html

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06. Actor-Critic with Advantage.html

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07. Summary.html

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07. Summary-hvYQ_3LgCYs.mp4

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index.html

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/Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/

01. Project Intro.html

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02. Quadcopter workspace.html

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03. Replay Buffer.html

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04. DDPG Actor.html

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05. DDPG Critic.html

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06. DDPG Agent.html

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07. Ornstein–Uhlenbeck Noise.html

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08. Troubleshooting.html

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index.html

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Project Description - Teach a Quadcopter How to Fly.html

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Project Rubric - Teach a Quadcopter How to Fly.html

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/Part 07-Module 01-Lesson 01_Enroll in your next Nanodegree program/

01. Enroll in your next ND program.html

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index.html

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/Part 08-Module 01-Lesson 01_Evaluation Metrics/

01. Intro.html

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02. Confusion Matrix.html

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02. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt

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02. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt

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03. Confusion Matrix 2.html

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04. Accuracy.html

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04. Accuracy-s6SfhPTNOHA.mp4

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05. Accuracy 2.html

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06. When accuracy won't work.html

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06. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt

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06. When Accuracy Wont Work-r0-O-gIDXZ0.mp4

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07. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt

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07. False Negatives and Positives.html

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08. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt

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08. Answer False Negatives And Positives-KOytJL1lvgg.mp4

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08. Precision and Recall.html

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09. 06 Precision SC V1-q2wVorBfefU.en.vtt

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09. 06 Precision SC V1-q2wVorBfefU.mp4

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09. Precision.html

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10. 07 Recall SC V1-0n5wUZiefkQ.en.vtt

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10. 07 Recall SC V1-0n5wUZiefkQ.mp4

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10. Recall.html

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11. ROC Curve-2Iw5TiGzJI4.en.vtt

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11. ROC Curve.html

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index.html

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/Part 08-Module 01-Lesson 02_Regression/

01. Intro.html

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01. Welcome To Linear Regression-zxZkTkM34BY.mp4

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02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt

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02. Quiz Housing Prices.html

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03. Solution Housing Prices.html

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04. Fitting A Line-gkdoknEEcaI.en.vtt

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06. Absolute Trick-DJWjBAqSkZw.en.vtt

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06. Absolute Trick.html

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07. Square Trick-AGZEq-yQgRM.en.vtt

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07. Square Trick.html

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08. Gradient Descent-4s4x9h6AN5Y.en.vtt

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08. Gradient Descent-4s4x9h6AN5Y.mp4

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08. Gradient Descent.html

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09. Mean Absolute Error.html

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09. Mean Absolute Error-vLKiY0Ehors.en.vtt

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09. Mean Absolute Error-vLKiY0Ehors.mp4

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09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt

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10. Mean Squared Error.html

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10. Mean Squared Error-MRyxmZDngI4.en.vtt

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10. Mean Squared Error-MRyxmZDngI4.mp4

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11. Minimizing Error Functions.html

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11. Minimizing Error Functions-RbT2TXN_6tY.mp4

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12. Mean vs Total Error.html

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13. Mini-batch Gradient Descent.html

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14. Absolute Error vs Squared Error.html

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14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt

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14. Absolute Vs Squared Error-csvdjaqt1GM.mp4

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14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.en.vtt

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14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4

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14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.en.vtt

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14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.mp4

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14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.pt-BR.vtt

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15. Linear Regression in scikit-learn.html

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16. Higher Dimensions.html

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16. Higher Dimensions--UvpQV1qmiE.en.vtt

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16. Higher Dimensions--UvpQV1qmiE.mp4

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16. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt

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17. Multiple Linear Regression.html

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18. Closed Form Solution-G3fRVgLa5gI.en.vtt

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18. Closed Form Solution-G3fRVgLa5gI.mp4

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18. Closed Form Solution.html

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19. (Optional) Closed form Solution Math.html

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20. Linear Regression Warnings.html

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21. Polynomial Regression-DBhWG-PagEQ.en.vtt

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21. Polynomial Regression.html

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22. Regularization.html

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22. Regularization-PyFNIcsNma0.en.vtt

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22. Regularization-PyFNIcsNma0.mp4

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22. Regularization-PyFNIcsNma0.pt-BR.vtt

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23. Neural Network Regression-aUJCBqBfEnI.mp4

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23. Neural Network Regression-aUJCBqBfEnI.pt-BR.vtt

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23. Neural Network Regression.html

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24. Neural Networks Playground.html

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25. Conclusion-pyeojf0NniQ.en.vtt

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25. Conclusion-pyeojf0NniQ.mp4

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25. Conclusion-pyeojf0NniQ.pt-BR.vtt

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25. Outro.html

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index.html

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/Part 08-Module 02-Lesson 01_MiniFlow/

01. Miniflow Introduction-Nqp_UifEwt0.en-US.vtt

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01. Miniflow Introduction-Nqp_UifEwt0.mp4

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01. Miniflow Introduction-Nqp_UifEwt0.pt-BR.vtt

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01. Miniflow Introduction-Nqp_UifEwt0.zh-CN.vtt

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01. Welcome to MiniFlow.html

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02. Graphs.html

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03. MiniFlow Architecture.html

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04. Forward Propagation.html

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05. Forward Propagation Solution.html

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06. Learning and Loss.html

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07. Linear Transform.html

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07. Pixels are Features!-qE5YYXtPq9U.en-US.vtt

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07. Pixels are Features!-qE5YYXtPq9U.mp4

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07. Pixels are Features!-qE5YYXtPq9U.pt-BR.vtt

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08. Sigmoid Function.html

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09. Cost.html

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10. Cost Solution.html

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11. Gradient Descent.html

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12. Backpropagation.html

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13. Stochastic Gradient Descent.html

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14. SGD Solution.html

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15. Outro.html

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index.html

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/

Discuss.FreeTutorials.Us.html

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FreeCoursesOnline.Me.html

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FreeTutorials.Eu.html

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index.rar

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Presented By SaM.txt

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[TGx]Downloaded from torrentgalaxy.org.txt

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Torrent Downloaded From GloDls.to.txt

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Total files 2644


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