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18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 194.0 MB
18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 166.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 143.1 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 124.5 MB
18. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 122.6 MB
18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 103.2 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 97.8 MB
18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.ogv 93.2 MB
4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 92.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 92.0 MB
5. Convolutional Neural Networks/5. CNN Architecture.mp4 90.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.ogv 90.4 MB
2. Google Colab/3. Uploading your own data to Google Colab.mp4 89.1 MB
18. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 88.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 87.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 87.2 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 86.5 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 86.4 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 86.3 MB
5. Convolutional Neural Networks/5. CNN Architecture.ogv 86.1 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.ogv 85.1 MB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 84.0 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 83.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 83.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.ogv 83.4 MB
18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.ogv 82.9 MB
18. Appendix FAQ/5. How to Code Yourself (part 1).mp4 82.1 MB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 80.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 80.0 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 78.2 MB
18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.mp4 77.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 77.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.ogv 77.1 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 77.0 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 76.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 76.1 MB
18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.ogv 74.4 MB
4. Feedforward Artificial Neural Networks/4. Activation Functions.ogv 73.4 MB
3. Machine Learning and Neurons/1. What is Machine Learning.mp4 73.2 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.ogv 73.0 MB
3. Machine Learning and Neurons/5. Regression Notebook.mp4 71.7 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 70.6 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 70.2 MB
18. Appendix FAQ/11. What order should I take your courses in (part 2).ogv 69.0 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 68.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).ogv 68.7 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 68.5 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).ogv 68.2 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.ogv 66.7 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 66.6 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).ogv 66.5 MB
3. Machine Learning and Neurons/3. Classification Notebook.mp4 66.3 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.ogv 65.9 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.ogv 65.5 MB
18. Appendix FAQ/5. How to Code Yourself (part 1).ogv 65.5 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 65.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 64.3 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 62.9 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 62.3 MB
3. Machine Learning and Neurons/1. What is Machine Learning.ogv 62.2 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 61.3 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 60.6 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.ogv 60.4 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 59.2 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 58.8 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).ogv 58.4 MB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 58.4 MB
7. Natural Language Processing (NLP)/1. Embeddings.mp4 58.0 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).ogv 56.9 MB
18. Appendix FAQ/6. How to Code Yourself (part 2).mp4 56.4 MB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 56.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 56.0 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 55.7 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.ogv 55.7 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.ogv 55.2 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 55.2 MB
3. Machine Learning and Neurons/7. How does a model learn.mp4 55.0 MB
7. Natural Language Processing (NLP)/1. Embeddings.ogv 54.9 MB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 54.8 MB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.ogv 54.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 53.6 MB
18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.ogv 53.3 MB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).ogv 52.8 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.ogv 52.6 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 52.5 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.ogv 52.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).ogv 52.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.ogv 51.9 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 51.6 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 51.1 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.ogv 50.9 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 50.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).ogv 50.7 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).ogv 50.7 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 50.2 MB
2. Google Colab/3. Uploading your own data to Google Colab.ogv 50.2 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 50.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).ogv 49.8 MB
3. Machine Learning and Neurons/6. The Neuron.mp4 49.4 MB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 49.3 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 49.2 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 48.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.ogv 48.1 MB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.ogv 48.0 MB
18. Appendix FAQ/10. What order should I take your courses in (part 1).ogv 47.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 47.2 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).ogv 46.9 MB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 46.9 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 46.8 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 46.4 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.ogv 46.3 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 46.1 MB
3. Machine Learning and Neurons/5. Regression Notebook.ogv 45.7 MB
3. Machine Learning and Neurons/7. How does a model learn.ogv 44.9 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.ogv 44.8 MB
18. Appendix FAQ/9. Is Theano Dead.mp4 44.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.ogv 44.0 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 43.8 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 43.3 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.ogv 43.2 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).ogv 43.2 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 43.0 MB
16. In-Depth Gradient Descent/5. Adam.mp4 42.6 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 42.5 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 42.4 MB
3. Machine Learning and Neurons/6. The Neuron.ogv 42.2 MB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.ogv 42.2 MB
3. Machine Learning and Neurons/8. Making Predictions.mp4 42.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.ogv 41.9 MB
16. In-Depth Gradient Descent/5. Adam.ogv 41.8 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).ogv 41.5 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.ogv 41.4 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).ogv 41.2 MB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.ogv 41.1 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.ogv 41.0 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 40.9 MB
18. Appendix FAQ/8. How to Succeed in this Course (Long Version).ogv 40.7 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.ogv 40.6 MB
18. Appendix FAQ/9. Is Theano Dead.ogv 40.4 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.ogv 40.3 MB
18. Appendix FAQ/6. How to Code Yourself (part 2).ogv 40.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.ogv 40.0 MB
16. In-Depth Gradient Descent/3. Momentum.mp4 39.4 MB
5. Convolutional Neural Networks/9. Data Augmentation.mp4 39.2 MB
1. Welcome/1. Introduction.mp4 39.2 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 39.0 MB
18. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp4 38.9 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 38.5 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.ogv 38.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 38.2 MB
15. In-Depth Loss Functions/1. Mean Squared Error.ogv 38.1 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 37.8 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 37.6 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 37.5 MB
3. Machine Learning and Neurons/3. Classification Notebook.ogv 37.5 MB
15. In-Depth Loss Functions/1. Mean Squared Error.mp4 37.3 MB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.ogv 37.3 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.ogv 36.6 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 36.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.ogv 36.5 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.ogv 36.4 MB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.ogv 36.4 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).ogv 36.3 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.ogv 36.2 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 36.1 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).ogv 35.6 MB
5. Convolutional Neural Networks/9. Data Augmentation.ogv 35.6 MB
16. In-Depth Gradient Descent/1. Gradient Descent.mp4 35.5 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 35.4 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 35.3 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.ogv 34.9 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 34.8 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.ogv 34.2 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.ogv 33.4 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.ogv 32.9 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 32.5 MB
16. In-Depth Gradient Descent/1. Gradient Descent.ogv 32.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.ogv 31.9 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 31.6 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 31.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 31.5 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 31.3 MB
1. Welcome/2. Outline.mp4 30.8 MB
1. Welcome/3. Where to get the code.mp4 30.5 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.ogv 30.4 MB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 30.3 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.ogv 29.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 29.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 29.7 MB
3. Machine Learning and Neurons/8. Making Predictions.ogv 29.3 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.ogv 29.2 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 28.9 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).ogv 28.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.ogv 28.4 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.ogv 27.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.ogv 27.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 27.4 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).ogv 26.9 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.ogv 26.7 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.ogv 26.3 MB
16. In-Depth Gradient Descent/3. Momentum.ogv 25.9 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.ogv 25.6 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).ogv 25.2 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 25.2 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.ogv 25.0 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 25.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.ogv 25.0 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).ogv 24.8 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).ogv 24.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 24.1 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.ogv 23.9 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.ogv 23.8 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).ogv 23.7 MB
5. Convolutional Neural Networks/10. Batch Normalization.mp4 23.4 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.ogv 23.0 MB
1. Welcome/3. Where to get the code.ogv 22.9 MB
1. Welcome/2. Outline.ogv 22.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.ogv 22.2 MB
5. Convolutional Neural Networks/10. Batch Normalization.ogv 21.6 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 21.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.ogv 21.2 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 20.9 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 20.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 20.4 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.ogv 20.3 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.ogv 20.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).ogv 20.1 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.ogv 19.4 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.ogv 19.2 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.ogv 19.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 18.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 18.2 MB
18. Appendix FAQ/1. What is the Appendix.mp4 18.0 MB
1. Welcome/1. Introduction.ogv 17.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).ogv 17.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.ogv 17.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.ogv 14.4 MB
18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.mp4 13.3 MB
18. Appendix FAQ/1. What is the Appendix.ogv 11.4 MB
18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.ogv 10.1 MB
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