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文档列表

9. Appendix/3. Windows-Focused Environment Setup 2018.mp4 195.2 MB
7. A3C/5. A3C - Code pt 4.mp4 193.3 MB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 102.0 MB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 97.9 MB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 91.2 MB
7. A3C/4. A3C - Code pt 3.mp4 88.6 MB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 85.4 MB
9. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
7. A3C/1. A3C - Theory and Outline.mp4 75.2 MB
7. A3C/3. A3C - Code pt 2.mp4 60.4 MB
7. A3C/2. A3C - Code pt 1 (Warmup).mp4 52.5 MB
9. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
9. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
9. Appendix/13. What order should I take your courses in (part 2).mp4 39.4 MB
9. Appendix/12. What order should I take your courses in (part 1).mp4 30.8 MB
9. Appendix/5. How to Code by Yourself (part 1).mp4 25.7 MB
5. Policy Gradients/9. Mountain Car Continuous Theano (v2).mp4 23.3 MB
5. Policy Gradients/7. Mountain Car Continuous Tensorflow.mp4 21.1 MB
6. Deep Q-Learning/7. Deep Q-Learning in Theano for Breakout.mp4 21.0 MB
5. Policy Gradients/6. Mountain Car Continuous Theano.mp4 20.0 MB
1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.mp4 19.8 MB
5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).mp4 19.7 MB
9. Appendix/7. How to Succeed in this Course (Long Version).mp4 19.2 MB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18.9 MB
5. Policy Gradients/1. Policy Gradient Methods.mp4 18.8 MB
9. Appendix/11. Is Theano Dead.mp4 18.7 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 17.3 MB
1. Introduction and Logistics/1. Introduction and Outline.mp4 16.6 MB
6. Deep Q-Learning/6. Deep Q-Learning in Tensorflow for Breakout.mp4 16.5 MB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15.7 MB
9. Appendix/6. How to Code by Yourself (part 2).mp4 15.5 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).mp4 15.4 MB
6. Deep Q-Learning/2. Deep Q-Learning Techniques.mp4 15.2 MB
6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.mp4 14.4 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).mp4 14.4 MB
5. Policy Gradients/3. Policy Gradient in Theano for CartPole.mp4 14.1 MB
2. Background Review/2. Review of Markov Decision Processes.mp4 12.9 MB
4. TD Lambda/3. TD Lambda.mp4 12.4 MB
2. Background Review/7. Review of Deep Learning.mp4 11.6 MB
6. Deep Q-Learning/9. Deep Q-Learning Section Summary.mp4 10.9 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.mp4 10.8 MB
4. TD Lambda/2. N-Step in Code.mp4 9.9 MB
7. A3C/7. Course Summary.mp4 9.9 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).mp4 9.3 MB
7. A3C/6. A3C - Section Summary.mp4 9.3 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.mp4 9.1 MB
6. Deep Q-Learning/5. Additional Implementation Details for Atari.mp4 8.9 MB
9. Appendix/10. Python 2 vs Python 3.mp4 8.2 MB
4. TD Lambda/4. TD Lambda in Code.mp4 8.0 MB
6. Deep Q-Learning/8. Partially Observable MDPs.mp4 8.0 MB
2. Background Review/5. Review of Temporal Difference Learning.mp4 7.5 MB
5. Policy Gradients/4. Continuous Action Spaces.mp4 6.9 MB
2. Background Review/3. Review of Dynamic Programming.mp4 6.8 MB
5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 6.8 MB
2. Background Review/4. Review of Monte Carlo Methods.mp4 6.5 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).mp4 6.3 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.mp4 6.2 MB
6. Deep Q-Learning/1. Deep Q-Learning Intro.mp4 6.2 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.mp4 6.1 MB
9. Appendix/1. What is the Appendix.mp4 5.7 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.mp4 5.6 MB
1. Introduction and Logistics/2. Where to get the Code.mp4 5.4 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5.3 MB
4. TD Lambda/1. N-Step Methods.mp4 5.3 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 4.8 MB
2. Background Review/1. Review Intro.mp4 4.4 MB
9. Appendix/2. Where to get Udemy coupons and FREE deep learning material.mp4 4.2 MB
2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.mp4 3.8 MB
4. TD Lambda/5. TD Lambda Summary.mp4 3.8 MB
5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3.5 MB
1. Introduction and Logistics/3. How to Succeed in this Course.mp4 3.5 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3.2 MB
9. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28.4 kB
9. Appendix/13. What order should I take your courses in (part 2).vtt 20.7 kB
9. Appendix/5. How to Code by Yourself (part 1).vtt 20.3 kB
7. A3C/5. A3C - Code pt 4.vtt 19.0 kB
7. A3C/1. A3C - Theory and Outline.vtt 18.3 kB
9. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.8 kB
9. Appendix/12. What order should I take your courses in (part 1).vtt 14.4 kB
5. Policy Gradients/1. Policy Gradient Methods.vtt 13.3 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.vtt 13.1 kB
9. Appendix/7. How to Succeed in this Course (Long Version).vtt 13.1 kB
1. Introduction and Logistics/1. Introduction and Outline.vtt 13.1 kB
9. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.7 kB
9. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12.5 kB
9. Appendix/6. How to Code by Yourself (part 2).vtt 11.9 kB
9. Appendix/11. Is Theano Dead.vtt 11.6 kB
6. Deep Q-Learning/2. Deep Q-Learning Techniques.vtt 11.0 kB
5. Policy Gradients/7. Mountain Car Continuous Tensorflow.vtt 9.2 kB
2. Background Review/2. Review of Markov Decision Processes.vtt 9.1 kB
5. Policy Gradients/6. Mountain Car Continuous Theano.vtt 8.8 kB
4. TD Lambda/3. TD Lambda.vtt 8.4 kB
2. Background Review/7. Review of Deep Learning.vtt 8.4 kB
7. A3C/4. A3C - Code pt 3.vtt 8.1 kB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.vtt 7.9 kB
5. Policy Gradients/9. Mountain Car Continuous Theano (v2).vtt 7.5 kB
7. A3C/3. A3C - Code pt 2.vtt 7.5 kB
6. Deep Q-Learning/7. Deep Q-Learning in Theano for Breakout.vtt 7.3 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).vtt 7.2 kB
7. A3C/2. A3C - Code pt 1 (Warmup).vtt 7.0 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.vtt 6.9 kB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.vtt 6.5 kB
5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).vtt 6.3 kB
6. Deep Q-Learning/5. Additional Implementation Details for Atari.vtt 6.3 kB
6. Deep Q-Learning/6. Deep Q-Learning in Tensorflow for Breakout.vtt 6.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.vtt 6.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).vtt 5.8 kB
9. Appendix/10. Python 2 vs Python 3.vtt 5.5 kB
7. A3C/7. Course Summary.vtt 5.5 kB
6. Deep Q-Learning/9. Deep Q-Learning Section Summary.vtt 5.4 kB
2. Background Review/5. Review of Temporal Difference Learning.vtt 5.4 kB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.vtt 5.2 kB
6. Deep Q-Learning/8. Partially Observable MDPs.vtt 5.2 kB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.vtt 5.2 kB
6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.vtt 4.9 kB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.vtt 4.9 kB
1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.vtt 4.9 kB
2. Background Review/3. Review of Dynamic Programming.vtt 4.8 kB
5. Policy Gradients/4. Continuous Action Spaces.vtt 4.8 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).vtt 4.7 kB
5. Policy Gradients/5. Mountain Car Continuous Specifics.vtt 4.6 kB
2. Background Review/4. Review of Monte Carlo Methods.vtt 4.5 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.vtt 4.4 kB
6. Deep Q-Learning/1. Deep Q-Learning Intro.vtt 4.3 kB
1. Introduction and Logistics/2. Where to get the Code.vtt 4.2 kB
5. Policy Gradients/3. Policy Gradient in Theano for CartPole.vtt 4.1 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.vtt 3.8 kB
4. TD Lambda/2. N-Step in Code.vtt 3.8 kB
1. Introduction and Logistics/3. How to Succeed in this Course.vtt 3.6 kB
4. TD Lambda/1. N-Step Methods.vtt 3.5 kB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.vtt 3.4 kB
9. Appendix/1. What is the Appendix.vtt 3.4 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).vtt 3.2 kB
2. Background Review/1. Review Intro.vtt 3.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.vtt 3.2 kB
9. Appendix/2. Where to get Udemy coupons and FREE deep learning material.vtt 3.1 kB
4. TD Lambda/4. TD Lambda in Code.vtt 3.0 kB
4. TD Lambda/5. TD Lambda Summary.vtt 2.7 kB
2. Background Review/6. Review of Approximation Methods for Reinforcement Learning.vtt 2.6 kB
7. A3C/6. A3C - Section Summary.vtt 2.4 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.vtt 2.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).vtt 2.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.vtt 2.1 kB
5. Policy Gradients/10. Policy Gradient Section Summary.vtt 1.7 kB
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