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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 0 |
463.91Кб |
| 1 |
229.21Кб |
| 1. Approximation Methods Section Introduction.mp4 |
22.08Мб |
| 1. Approximation Methods Section Introduction-en_US.srt |
5.60Кб |
| 1. Beginners, halt! Stop here if you skipped ahead.mp4 |
83.78Мб |
| 1. Beginners, halt! Stop here if you skipped ahead-en_US.srt |
19.90Кб |
| 1. Dynamic Programming Section Introduction.mp4 |
34.67Мб |
| 1. Dynamic Programming Section Introduction-en_US.srt |
11.91Кб |
| 1. How to Code by Yourself (part 1).mp4 |
24.53Мб |
| 1. How to Code by Yourself (part 1)-en_US.srt |
25.95Кб |
| 1. How to Succeed in this Course (Long Version).mp4 |
18.31Мб |
| 1. How to Succeed in this Course (Long Version)-en_US.srt |
14.00Кб |
| 1. Introduction.mp4 |
34.24Мб |
| 1. Introduction-en_US.srt |
4.03Кб |
| 1. MDP Section Introduction.mp4 |
37.20Мб |
| 1. MDP Section Introduction-en_US.srt |
8.02Кб |
| 1. Monte Carlo Intro.mp4 |
47.59Мб |
| 1. Monte Carlo Intro-en_US.srt |
12.12Кб |
| 1. Section Introduction The Explore-Exploit Dilemma.mp4 |
51.99Мб |
| 1. Section Introduction The Explore-Exploit Dilemma-en_US.srt |
12.99Кб |
| 1. Temporal Difference Introduction.mp4 |
14.44Мб |
| 1. Temporal Difference Introduction-en_US.srt |
5.04Кб |
| 1. This Course vs. RL Book What's the Difference.mp4 |
38.21Мб |
| 1. This Course vs. RL Book What's the Difference-en_US.srt |
9.89Кб |
| 1. What is Reinforcement Learning.mp4 |
54.62Мб |
| 1. What is Reinforcement Learning-en_US.srt |
10.51Кб |
| 1. What is the Appendix.mp4 |
5.45Мб |
| 1. What is the Appendix-en_US.srt |
3.58Кб |
| 1. Windows-Focused Environment Setup 2018.mp4 |
186.38Мб |
| 1. Windows-Focused Environment Setup 2018-en_US.srt |
19.29Кб |
| 10 |
280.95Кб |
| 10. Approximation Methods Exercise.mp4 |
17.53Мб |
| 10. Approximation Methods Exercise-en_US.srt |
5.13Кб |
| 10. Optimistic Initial Values Beginner's Exercise Prompt.mp4 |
13.77Мб |
| 10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt |
2.82Кб |
| 10. Policy Iteration in Code.mp4 |
56.38Мб |
| 10. Policy Iteration in Code-en_US.srt |
10.39Кб |
| 10. Stock Trading Project Discussion.mp4 |
15.78Мб |
| 10. Stock Trading Project Discussion-en_US.srt |
4.19Кб |
| 10. The Bellman Equation (pt 3).mp4 |
24.67Мб |
| 10. The Bellman Equation (pt 3)-en_US.srt |
7.39Кб |
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180.48Кб |
| 11. Approximation Methods Section Summary.mp4 |
21.75Мб |
| 11. Approximation Methods Section Summary-en_US.srt |
3.85Кб |
| 11. Bellman Examples.mp4 |
87.12Мб |
| 11. Bellman Examples-en_US.srt |
26.62Кб |
| 11. Optimistic Initial Values Code.mp4 |
24.57Мб |
| 11. Optimistic Initial Values Code-en_US.srt |
4.99Кб |
| 11. Policy Iteration in Windy Gridworld.mp4 |
51.41Мб |
| 11. Policy Iteration in Windy Gridworld-en_US.srt |
10.57Кб |
| 12 |
637.71Кб |
| 12. Optimal Policy and Optimal Value Function (pt 1).mp4 |
56.06Мб |
| 12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt |
11.04Кб |
| 12. UCB1 Theory.mp4 |
55.53Мб |
| 12. UCB1 Theory-en_US.srt |
19.19Кб |
| 12. Value Iteration.mp4 |
35.27Мб |
| 12. Value Iteration-en_US.srt |
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| 13. Optimal Policy and Optimal Value Function (pt 2).mp4 |
15.72Мб |
| 13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt |
4.91Кб |
| 13. UCB1 Beginner's Exercise Prompt.mp4 |
12.74Мб |
| 13. UCB1 Beginner's Exercise Prompt-en_US.srt |
2.65Кб |
| 13. Value Iteration in Code.mp4 |
45.67Мб |
| 13. Value Iteration in Code-en_US.srt |
8.51Кб |
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97.75Кб |
| 14. Dynamic Programming Summary.mp4 |
25.11Мб |
| 14. Dynamic Programming Summary-en_US.srt |
6.28Кб |
| 14. MDP Summary.mp4 |
14.28Мб |
| 14. MDP Summary-en_US.srt |
3.46Кб |
| 14. UCB1 Code.mp4 |
20.66Мб |
| 14. UCB1 Code-en_US.srt |
3.59Кб |
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485.31Кб |
| 15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 |
55.90Мб |
| 15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt |
16.13Кб |
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| 16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 |
74.50Мб |
| 16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt |
22.71Кб |
| 17 |
10.86Кб |
| 17. Thompson Sampling Beginner's Exercise Prompt.mp4 |
17.89Мб |
| 17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt |
3.26Кб |
| 18 |
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| 18. Thompson Sampling Code.mp4 |
32.83Мб |
| 18. Thompson Sampling Code-en_US.srt |
5.43Кб |
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| 19. Thompson Sampling With Gaussian Reward Theory.mp4 |
48.51Мб |
| 19. Thompson Sampling With Gaussian Reward Theory-en_US.srt |
14.41Кб |
| 2 |
697.20Кб |
| 2. Applications of the Explore-Exploit Dilemma.mp4 |
51.18Мб |
| 2. Applications of the Explore-Exploit Dilemma-en_US.srt |
10.50Кб |
| 2. BONUS Where to get discount coupons and FREE deep learning material.mp4 |
37.83Мб |
| 2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt |
7.57Кб |
| 2. Course Outline and Big Picture.mp4 |
39.68Мб |
| 2. Course Outline and Big Picture-en_US.srt |
10.04Кб |
| 2. From Bandits to Full Reinforcement Learning.mp4 |
41.19Мб |
| 2. From Bandits to Full Reinforcement Learning-en_US.srt |
11.59Кб |
| 2. Gridworld.mp4 |
53.99Мб |
| 2. Gridworld-en_US.srt |
16.65Кб |
| 2. How to Code by Yourself (part 2).mp4 |
14.80Мб |
| 2. How to Code by Yourself (part 2)-en_US.srt |
15.79Кб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.92Мб |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt |
15.71Кб |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
38.95Мб |
| 2. Iterative Policy Evaluation.mp4 |
60.82Мб |
| 2. Iterative Policy Evaluation-en_US.srt |
20.37Кб |
| 2. Linear Models for Reinforcement Learning.mp4 |
31.08Мб |
| 2. Linear Models for Reinforcement Learning-en_US.srt |
10.97Кб |
| 2. Monte Carlo Policy Evaluation.mp4 |
47.15Мб |
| 2. Monte Carlo Policy Evaluation-en_US.srt |
14.07Кб |
| 2. Stock Trading Project Section Introduction.mp4 |
26.76Мб |
| 2. Stock Trading Project Section Introduction-en_US.srt |
6.58Кб |
| 2. TD(0) Prediction.mp4 |
15.79Мб |
| 2. TD(0) Prediction-en_US.srt |
6.64Кб |
| 20 |
5.42Кб |
| 20. Thompson Sampling With Gaussian Reward Code.mp4 |
43.43Мб |
| 20. Thompson Sampling With Gaussian Reward Code-en_US.srt |
6.97Кб |
| 21 |
361.41Кб |
| 21. Why don't we just use a library.mp4 |
27.40Мб |
| 21. Why don't we just use a library-en_US.srt |
7.32Кб |
| 22 |
606.45Кб |
| 22. Nonstationary Bandits.mp4 |
30.98Мб |
| 22. Nonstationary Bandits-en_US.srt |
9.21Кб |
| 23 |
840.72Кб |
| 23. Bandit Summary, Real Data, and Online Learning.mp4 |
34.61Мб |
| 23. Bandit Summary, Real Data, and Online Learning-en_US.srt |
8.76Кб |
| 24 |
677.26Кб |
| 24. (Optional) Alternative Bandit Designs.mp4 |
50.34Мб |
| 24. (Optional) Alternative Bandit Designs-en_US.srt |
13.93Кб |
| 25 |
282.56Кб |
| 25. Suggestion Box.mp4 |
16.13Мб |
| 25. Suggestion Box-en_US.srt |
4.50Кб |
| 26 |
505.47Кб |
| 27 |
419.86Кб |
| 28 |
871.86Кб |
| 29 |
71.52Кб |
| 3 |
316.54Кб |
| 3. Choosing Rewards.mp4 |
32.49Мб |
| 3. Choosing Rewards-en_US.srt |
5.23Кб |
| 3. Data and Environment.mp4 |
52.01Мб |
| 3. Data and Environment-en_US.srt |
15.11Кб |
| 3. Designing Your RL Program.mp4 |
22.34Мб |
| 3. Designing Your RL Program-en_US.srt |
6.39Кб |
| 3. Epsilon-Greedy Theory.mp4 |
28.30Мб |
| 3. Epsilon-Greedy Theory-en_US.srt |
9.05Кб |
| 3. External URLs.txt |
75б |
| 3. Feature Engineering.mp4 |
45.88Мб |
| 3. Feature Engineering-en_US.srt |
13.90Кб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
29.32Мб |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt |
15.45Кб |
| 3. Monte Carlo Policy Evaluation in Code.mp4 |
51.65Мб |
| 3. Monte Carlo Policy Evaluation in Code-en_US.srt |
10.17Кб |
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.32Мб |
| 3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt |
13.54Кб |
| 3. TD(0) Prediction in Code.mp4 |
32.43Мб |
| 3. TD(0) Prediction in Code-en_US.srt |
5.80Кб |
| 3. Where to get the Code.mp4 |
22.72Мб |
| 3. Where to get the Code-en_US.srt |
6.05Кб |
| 30 |
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| 4 |
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| 4. Approximation Methods for Prediction.mp4 |
34.34Мб |
| 4. Approximation Methods for Prediction-en_US.srt |
12.06Кб |
| 4. Calculating a Sample Mean (pt 1).mp4 |
23.13Мб |
| 4. Calculating a Sample Mean (pt 1)-en_US.srt |
7.22Кб |
| 4. Gridworld in Code.mp4 |
46.79Мб |
| 4. Gridworld in Code-en_US.srt |
15.72Кб |
| 4. How to Model Q for Q-Learning.mp4 |
44.89Мб |
| 4. How to Model Q for Q-Learning-en_US.srt |
11.58Кб |
| 4. How to Succeed in this Course.mp4 |
43.82Мб |
| 4. How to Succeed in this Course-en_US.srt |
7.94Кб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
37.62Мб |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt |
22.21Кб |
| 4. Monte Carlo Control.mp4 |
35.61Мб |
| 4. Monte Carlo Control-en_US.srt |
11.16Кб |
| 4. Python 2 vs Python 3.mp4 |
7.83Мб |
| 4. Python 2 vs Python 3-en_US.srt |
5.86Кб |
| 4. SARSA.mp4 |
16.22Мб |
| 4. SARSA-en_US.srt |
5.77Кб |
| 4. The Markov Property.mp4 |
21.76Мб |
| 4. The Markov Property-en_US.srt |
7.70Кб |
| 40 |
588.54Кб |
| 41 |
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| 42 |
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830.00Кб |
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512.09Кб |
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| 48 |
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| 49 |
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| 5 |
583.75Кб |
| 5. Approximation Methods for Prediction Code.mp4 |
62.29Мб |
| 5. Approximation Methods for Prediction Code-en_US.srt |
10.20Кб |
| 5. Design of the Program.mp4 |
23.31Мб |
| 5. Design of the Program-en_US.srt |
8.19Кб |
| 5. Epsilon-Greedy Beginner's Exercise Prompt.mp4 |
28.66Мб |
| 5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt |
6.15Кб |
| 5. Iterative Policy Evaluation in Code.mp4 |
68.43Мб |
| 5. Iterative Policy Evaluation in Code-en_US.srt |
15.64Кб |
| 5. Markov Decision Processes (MDPs).mp4 |
61.73Мб |
| 5. Markov Decision Processes (MDPs)-en_US.srt |
18.85Кб |
| 5. Monte Carlo Control in Code.mp4 |
64.41Мб |
| 5. Monte Carlo Control in Code-en_US.srt |
10.74Кб |
| 5. SARSA in Code.mp4 |
44.90Мб |
| 5. SARSA in Code-en_US.srt |
7.40Кб |
| 5. Warmup.mp4 |
62.60Мб |
| 5. Warmup-en_US.srt |
18.14Кб |
| 50 |
174.93Кб |
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403.54Кб |
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| 6 |
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| 6. Approximation Methods for Control.mp4 |
17.59Мб |
| 6. Approximation Methods for Control-en_US.srt |
5.49Кб |
| 6. Code pt 1.mp4 |
49.72Мб |
| 6. Code pt 1-en_US.srt |
9.28Кб |
| 6. Designing Your Bandit Program.mp4 |
24.51Мб |
| 6. Designing Your Bandit Program-en_US.srt |
5.41Кб |
| 6. Future Rewards.mp4 |
39.50Мб |
| 6. Future Rewards-en_US.srt |
12.19Кб |
| 6. Monte Carlo Control without Exploring Starts.mp4 |
23.40Мб |
| 6. Monte Carlo Control without Exploring Starts-en_US.srt |
5.60Кб |
| 6. Q Learning.mp4 |
19.82Мб |
| 6. Q Learning-en_US.srt |
6.09Кб |
| 6. Windy Gridworld in Code.mp4 |
41.45Мб |
| 6. Windy Gridworld in Code-en_US.srt |
10.04Кб |
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| 7. Approximation Methods for Control Code.mp4 |
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| 7. Approximation Methods for Control Code-en_US.srt |
10.55Кб |
| 7. Code pt 2.mp4 |
65.29Мб |
| 7. Code pt 2-en_US.srt |
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| 7. Epsilon-Greedy in Code.mp4 |
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| 7. Epsilon-Greedy in Code-en_US.srt |
8.30Кб |
| 7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 |
46.93Мб |
| 7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt |
9.34Кб |
| 7. Monte Carlo Control without Exploring Starts in Code.mp4 |
40.69Мб |
| 7. Monte Carlo Control without Exploring Starts in Code-en_US.srt |
6.91Кб |
| 7. Q Learning in Code.mp4 |
38.55Мб |
| 7. Q Learning in Code-en_US.srt |
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| 7. Value Functions.mp4 |
18.55Мб |
| 7. Value Functions-en_US.srt |
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| 8. CartPole.mp4 |
26.90Мб |
| 8. CartPole-en_US.srt |
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| 8. Code pt 3.mp4 |
33.72Мб |
| 8. Code pt 3-en_US.srt |
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| 8. Comparing Different Epsilons.mp4 |
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| 8. Comparing Different Epsilons-en_US.srt |
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| 8. Monte Carlo Summary.mp4 |
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| 8. Monte Carlo Summary-en_US.srt |
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| 8. Policy Improvement.mp4 |
43.99Мб |
| 8. Policy Improvement-en_US.srt |
14.18Кб |
| 8. TD Learning Section Summary.mp4 |
10.04Мб |
| 8. TD Learning Section Summary-en_US.srt |
2.85Кб |
| 8. The Bellman Equation (pt 1).mp4 |
27.78Мб |
| 8. The Bellman Equation (pt 1)-en_US.srt |
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| 9. CartPole Code.mp4 |
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| 9. CartPole Code-en_US.srt |
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| 9. Code pt 4.mp4 |
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| 9. Code pt 4-en_US.srt |
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| 9. Optimistic Initial Values Theory.mp4 |
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| 9. Optimistic Initial Values Theory-en_US.srt |
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| 9. Policy Iteration.mp4 |
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| 9. Policy Iteration-en_US.srt |
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| 9. The Bellman Equation (pt 2).mp4 |
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| 9. The Bellman Equation (pt 2)-en_US.srt |
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| TutsNode.com.txt |
63б |