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文档列表

18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 203.4 MB
18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 174.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 150.1 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 130.5 MB
18. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 128.6 MB
18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 122.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 108.2 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 102.5 MB
18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.ogv 97.8 MB
4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 96.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 96.5 MB
5. Convolutional Neural Networks/5. CNN Architecture.mp4 95.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.ogv 94.8 MB
2. Google Colab/3. Uploading your own data to Google Colab.mp4 93.4 MB
18. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 92.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 91.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 91.5 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 90.7 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 90.5 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 90.5 MB
5. Convolutional Neural Networks/5. CNN Architecture.ogv 90.3 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.ogv 89.3 MB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 88.0 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 87.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 87.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.ogv 87.4 MB
18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.ogv 87.0 MB
18. Appendix FAQ/5. How to Code Yourself (part 1).mp4 86.1 MB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 84.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 83.9 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 82.0 MB
18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.mp4 81.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 81.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.ogv 80.9 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 80.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 80.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 79.8 MB
18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.ogv 78.0 MB
4. Feedforward Artificial Neural Networks/4. Activation Functions.ogv 77.0 MB
3. Machine Learning and Neurons/1. What is Machine Learning.mp4 76.7 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.ogv 76.5 MB
3. Machine Learning and Neurons/5. Regression Notebook.mp4 75.2 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 74.0 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 73.6 MB
18. Appendix FAQ/11. What order should I take your courses in (part 2).ogv 72.4 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 72.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).ogv 72.0 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 71.8 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).ogv 71.5 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.ogv 70.0 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 69.8 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).ogv 69.7 MB
3. Machine Learning and Neurons/3. Classification Notebook.mp4 69.5 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.ogv 69.1 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.ogv 68.7 MB
18. Appendix FAQ/5. How to Code Yourself (part 1).ogv 68.7 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 68.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 67.5 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 66.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 65.4 MB
3. Machine Learning and Neurons/1. What is Machine Learning.ogv 65.2 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 64.3 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 63.5 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.ogv 63.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 62.0 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 61.6 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).ogv 61.3 MB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 61.2 MB
7. Natural Language Processing (NLP)/1. Embeddings.mp4 60.8 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).ogv 59.7 MB
18. Appendix FAQ/6. How to Code Yourself (part 2).mp4 59.1 MB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 58.9 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 58.7 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 58.4 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.ogv 58.4 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.ogv 57.9 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 57.8 MB
3. Machine Learning and Neurons/7. How does a model learn.mp4 57.7 MB
7. Natural Language Processing (NLP)/1. Embeddings.ogv 57.6 MB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 57.5 MB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.ogv 56.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 56.2 MB
18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.ogv 55.9 MB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).ogv 55.4 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.ogv 55.1 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 55.1 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.ogv 55.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).ogv 54.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.ogv 54.4 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 54.2 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 53.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.ogv 53.4 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 53.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).ogv 53.1 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).ogv 53.1 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 52.7 MB
2. Google Colab/3. Uploading your own data to Google Colab.ogv 52.7 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 52.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).ogv 52.3 MB
3. Machine Learning and Neurons/6. The Neuron.mp4 51.8 MB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 51.7 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 51.6 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 51.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.ogv 50.5 MB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.ogv 50.3 MB
18. Appendix FAQ/10. What order should I take your courses in (part 1).ogv 50.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 49.5 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).ogv 49.2 MB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 49.1 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 49.1 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 48.7 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.ogv 48.5 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 48.3 MB
3. Machine Learning and Neurons/5. Regression Notebook.ogv 47.9 MB
3. Machine Learning and Neurons/7. How does a model learn.ogv 47.1 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.ogv 47.0 MB
18. Appendix FAQ/9. Is Theano Dead.mp4 46.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.ogv 46.2 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 46.0 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 45.4 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.ogv 45.3 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).ogv 45.3 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 45.1 MB
16. In-Depth Gradient Descent/5. Adam.mp4 44.6 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 44.6 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 44.4 MB
3. Machine Learning and Neurons/6. The Neuron.ogv 44.2 MB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.ogv 44.2 MB
3. Machine Learning and Neurons/8. Making Predictions.mp4 44.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.ogv 44.0 MB
16. In-Depth Gradient Descent/5. Adam.ogv 43.9 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).ogv 43.5 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.ogv 43.4 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).ogv 43.2 MB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.ogv 43.1 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.ogv 43.0 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 42.8 MB
18. Appendix FAQ/8. How to Succeed in this Course (Long Version).ogv 42.7 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.ogv 42.6 MB
18. Appendix FAQ/9. Is Theano Dead.ogv 42.3 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.ogv 42.3 MB
18. Appendix FAQ/6. How to Code Yourself (part 2).ogv 42.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.ogv 41.9 MB
16. In-Depth Gradient Descent/3. Momentum.mp4 41.3 MB
5. Convolutional Neural Networks/9. Data Augmentation.mp4 41.1 MB
1. Welcome/1. Introduction.mp4 41.1 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 40.9 MB
18. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp4 40.8 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 40.4 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.ogv 40.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 40.0 MB
15. In-Depth Loss Functions/1. Mean Squared Error.ogv 39.9 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 39.6 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 39.4 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 39.3 MB
3. Machine Learning and Neurons/3. Classification Notebook.ogv 39.3 MB
15. In-Depth Loss Functions/1. Mean Squared Error.mp4 39.2 MB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.ogv 39.1 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.ogv 38.4 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 38.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.ogv 38.2 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.ogv 38.2 MB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.ogv 38.2 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).ogv 38.1 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.ogv 37.9 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 37.9 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).ogv 37.4 MB
5. Convolutional Neural Networks/9. Data Augmentation.ogv 37.3 MB
16. In-Depth Gradient Descent/1. Gradient Descent.mp4 37.3 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 37.2 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 37.0 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.ogv 36.6 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 36.5 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.ogv 35.8 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.ogv 35.1 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.ogv 34.5 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 34.1 MB
16. In-Depth Gradient Descent/1. Gradient Descent.ogv 33.7 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.ogv 33.5 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 33.1 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 33.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 33.0 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 32.8 MB
1. Welcome/2. Outline.mp4 32.3 MB
1. Welcome/3. Where to get the code.mp4 32.0 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.ogv 31.8 MB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 31.8 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.ogv 31.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 31.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 31.1 MB
3. Machine Learning and Neurons/8. Making Predictions.ogv 30.7 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.ogv 30.6 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 30.3 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).ogv 30.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.ogv 29.8 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.ogv 29.1 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.ogv 28.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 28.8 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).ogv 28.2 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.ogv 28.0 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.ogv 27.5 MB
16. In-Depth Gradient Descent/3. Momentum.ogv 27.2 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.ogv 26.8 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).ogv 26.5 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 26.4 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.ogv 26.3 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 26.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.ogv 26.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).ogv 26.0 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).ogv 25.5 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 25.2 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.ogv 25.0 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.ogv 24.9 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).ogv 24.8 MB
5. Convolutional Neural Networks/10. Batch Normalization.mp4 24.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.ogv 24.1 MB
1. Welcome/3. Where to get the code.ogv 24.0 MB
1. Welcome/2. Outline.ogv 23.7 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.ogv 23.3 MB
5. Convolutional Neural Networks/10. Batch Normalization.ogv 22.7 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 22.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.ogv 22.3 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 22.0 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 21.4 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.ogv 21.2 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.ogv 21.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).ogv 21.1 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.ogv 20.4 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.ogv 20.1 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.ogv 20.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 19.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 19.1 MB
18. Appendix FAQ/1. What is the Appendix.mp4 18.9 MB
1. Welcome/1. Introduction.ogv 18.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).ogv 18.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.ogv 17.9 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.ogv 15.1 MB
18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.mp4 13.9 MB
18. Appendix FAQ/1. What is the Appendix.ogv 11.9 MB
18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.ogv 10.6 MB
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