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文档列表

6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.mp4 246.0 MB
6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.mp4 245.0 MB
9. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.mp4 195.2 MB
7. A3C/5. A3C - Code pt 4.mp4 193.3 MB
2. The Basics of Reinforcement Learning/2. Elements of a Reinforcement Learning Problem.mp4 110.4 MB
7. A3C/4. A3C - Code pt 3.mp4 88.6 MB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 82.3 MB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.mp4 81.9 MB
7. A3C/1. A3C - Theory and Outline.mp4 75.2 MB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.mp4 71.0 MB
2. The Basics of Reinforcement Learning/11. Q-Learning.mp4 70.3 MB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.mp4 66.5 MB
7. A3C/3. A3C - Code pt 2.mp4 60.4 MB
2. The Basics of Reinforcement Learning/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 60.1 MB
1. Introduction and Logistics/2. Where to get the Code.mp4 54.1 MB
2. The Basics of Reinforcement Learning/4. Markov Decision Processes (MDPs).mp4 53.3 MB
1. Introduction and Logistics/1. Introduction and Outline.mp4 52.9 MB
7. A3C/2. A3C - Code pt 1 (Warmup).mp4 52.5 MB
2. The Basics of Reinforcement Learning/6. Value Functions and the Bellman Equation.mp4 50.5 MB
2. The Basics of Reinforcement Learning/3. States, Actions, Rewards, Policies.mp4 46.7 MB
9. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
1. Introduction and Logistics/3. How to Succeed in this Course.mp4 45.9 MB
2. The Basics of Reinforcement Learning/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 45.0 MB
2. The Basics of Reinforcement Learning/1. Reinforcement Learning Section Introduction.mp4 43.0 MB
2. The Basics of Reinforcement Learning/12. How to Learn Reinforcement Learning.mp4 42.5 MB
2. The Basics of Reinforcement Learning/10. Epsilon-Greedy.mp4 42.1 MB
12. Appendix FAQ Finale/2. BONUS.mp4 39.7 MB
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 39.5 MB
2. The Basics of Reinforcement Learning/7. What does it mean to “learn”.mp4 33.4 MB
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 30.8 MB
6. Deep Q-Learning/6. Pseudocode and Replay Memory.mp4 29.2 MB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 25.7 MB
2. The Basics of Reinforcement Learning/5. The Return.mp4 24.9 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
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
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.mp4 18.8 MB
5. Policy Gradients/1. Policy Gradient Methods.mp4 18.8 MB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp4 18.7 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.mp4 17.3 MB
2. The Basics of Reinforcement Learning/13. Suggestion Box.mp4 16.9 MB
4. TD Lambda/1. N-Step Methods.mp4 16.3 MB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.mp4 15.7 MB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. 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 15.1 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
4. TD Lambda/3. TD Lambda.mp4 12.4 MB
6. Deep Q-Learning/10. 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
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. 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/9. Partially Observable MDPs.mp4 8.0 MB
5. Policy Gradients/4. Continuous Action Spaces.mp4 6.9 MB
5. Policy Gradients/5. Mountain Car Continuous Specifics.mp4 6.8 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
12. Appendix FAQ Finale/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
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.mp4 5.3 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.mp4 4.8 MB
4. TD Lambda/5. TD Lambda Summary.mp4 3.8 MB
5. Policy Gradients/10. Policy Gradient Section Summary.mp4 3.5 MB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).mp4 3.2 MB
6. Deep Q-Learning/7. Deep Q-Learning in Tensorflow for Breakout.srt 28.8 kB
6. Deep Q-Learning/8. Deep Q-Learning in Theano for Breakout.srt 28.8 kB
2. The Basics of Reinforcement Learning/2. Elements of a Reinforcement Learning Problem.srt 27.8 kB
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.6 kB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).srt 23.3 kB
7. A3C/5. A3C - Code pt 4.srt 21.7 kB
7. A3C/1. A3C - Theory and Outline.srt 20.7 kB
9. Setting Up Your Environment (FAQ by Student Request)/1. Anaconda Environment Setup.srt 20.6 kB
2. The Basics of Reinforcement Learning/11. Q-Learning.srt 19.4 kB
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.4 kB
2. The Basics of Reinforcement Learning/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.9 kB
5. Policy Gradients/1. Policy Gradient Methods.srt 15.2 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/6. RBF Neural Networks.srt 15.0 kB
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 14.9 kB
9. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.8 kB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
2. The Basics of Reinforcement Learning/4. Markov Decision Processes (MDPs).srt 13.6 kB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).srt 13.6 kB
1. Introduction and Logistics/2. Where to get the Code.srt 13.5 kB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.srt 13.2 kB
2. The Basics of Reinforcement Learning/6. Value Functions and the Bellman Equation.srt 13.2 kB
2. The Basics of Reinforcement Learning/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.7 kB
6. Deep Q-Learning/2. Deep Q-Learning Techniques.srt 12.6 kB
2. The Basics of Reinforcement Learning/3. States, Actions, Rewards, Policies.srt 12.0 kB
1. Introduction and Logistics/1. Introduction and Outline.srt 11.6 kB
5. Policy Gradients/7. Mountain Car Continuous Tensorflow.srt 10.5 kB
5. Policy Gradients/6. Mountain Car Continuous Theano.srt 10.1 kB
4. TD Lambda/3. TD Lambda.srt 9.6 kB
7. A3C/4. A3C - Code pt 3.srt 9.2 kB
2. The Basics of Reinforcement Learning/7. What does it mean to “learn”.srt 9.1 kB
2. The Basics of Reinforcement Learning/1. Reinforcement Learning Section Introduction.srt 9.0 kB
5. Policy Gradients/2. Policy Gradient in TensorFlow for CartPole.srt 8.9 kB
5. Policy Gradients/9. Mountain Car Continuous Theano (v2).srt 8.5 kB
7. A3C/3. A3C - Code pt 2.srt 8.5 kB
1. Introduction and Logistics/3. How to Succeed in this Course.srt 8.5 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/5. CartPole with Bins (Code).srt 8.2 kB
12. Appendix FAQ Finale/2. BONUS.srt 8.1 kB
2. The Basics of Reinforcement Learning/12. How to Learn Reinforcement Learning.srt 8.0 kB
6. Deep Q-Learning/6. Pseudocode and Replay Memory.srt 8.0 kB
7. A3C/2. A3C - Code pt 1 (Warmup).srt 8.0 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/1. OpenAI Gym Tutorial.srt 7.9 kB
2. The Basics of Reinforcement Learning/10. Epsilon-Greedy.srt 7.7 kB
8. Theano and Tensorflow Basics Review/1. (Review) Theano Basics.srt 7.5 kB
5. Policy Gradients/8. Mountain Car Continuous Tensorflow (v2).srt 7.2 kB
6. Deep Q-Learning/5. Additional Implementation Details for Atari.srt 7.1 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/2. Random Search.srt 7.0 kB
2. The Basics of Reinforcement Learning/5. The Return.srt 6.8 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/7. RBF Networks with Mountain Car (Code).srt 6.6 kB
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.srt 6.2 kB
6. Deep Q-Learning/10. Deep Q-Learning Section Summary.srt 6.2 kB
7. A3C/7. Course Summary.srt 6.2 kB
8. Theano and Tensorflow Basics Review/4. (Review) Tensorflow Neural Network in Code.srt 6.1 kB
8. Theano and Tensorflow Basics Review/3. (Review) Tensorflow Basics.srt 6.1 kB
6. Deep Q-Learning/3. Deep Q-Learning in Tensorflow for CartPole.srt 6.0 kB
6. Deep Q-Learning/9. Partially Observable MDPs.srt 5.9 kB
6. Deep Q-Learning/4. Deep Q-Learning in Theano for CartPole.srt 5.5 kB
1. Introduction and Logistics/4. Tensorflow or Theano - Your Choice!.srt 5.5 kB
5. Policy Gradients/4. Continuous Action Spaces.srt 5.4 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/4. CartPole with Bins (Theory).srt 5.3 kB
5. Policy Gradients/5. Mountain Car Continuous Specifics.srt 5.1 kB
6. Deep Q-Learning/1. Deep Q-Learning Intro.srt 5.0 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/12. Plugging in a Neural Network.srt 4.9 kB
2. The Basics of Reinforcement Learning/13. Suggestion Box.srt 4.8 kB
5. Policy Gradients/3. Policy Gradient in Theano for CartPole.srt 4.6 kB
4. TD Lambda/2. N-Step in Code.srt 4.3 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/13. OpenAI Gym Section Summary.srt 4.3 kB
8. Theano and Tensorflow Basics Review/2. (Review) Theano Neural Network in Code.srt 4.0 kB
4. TD Lambda/1. N-Step Methods.srt 3.9 kB
12. Appendix FAQ Finale/1. What is the Appendix.srt 3.8 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/9. RBF Networks with CartPole (Code).srt 3.7 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/10. Theano Warmup.srt 3.6 kB
4. TD Lambda/4. TD Lambda in Code.srt 3.4 kB
4. TD Lambda/5. TD Lambda Summary.srt 3.0 kB
7. A3C/6. A3C - Section Summary.srt 2.7 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/11. Tensorflow Warmup.srt 2.5 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/8. RBF Networks with CartPole (Theory).srt 2.5 kB
3. OpenAI Gym and Basic Reinforcement Learning Techniques/3. Saving a Video.srt 2.4 kB
5. Policy Gradients/10. Policy Gradient Section Summary.srt 1.9 kB
1. Introduction and Logistics/[Tutorialsplanet.NET].url 128 Bytes
10. Extra Help With Python Coding for Beginners (FAQ by Student Request)/[Tutorialsplanet.NET].url 128 Bytes
[Tutorialsplanet.NET].url 128 Bytes
1. Introduction and Logistics/2.1 Github Link.html 120 Bytes
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 0 Bytes
11. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 0 Bytes

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