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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
463.91KB |
| 1 |
229.21KB |
| 1. Approximation Methods Section Introduction.mp4 |
22.08MB |
| 1. Approximation Methods Section Introduction-en_US.srt |
5.60KB |
| 1. Beginners, halt! Stop here if you skipped ahead.mp4 |
83.78MB |
| 1. Beginners, halt! Stop here if you skipped ahead-en_US.srt |
19.90KB |
| 1. Dynamic Programming Section Introduction.mp4 |
34.67MB |
| 1. Dynamic Programming Section Introduction-en_US.srt |
11.91KB |
| 1. How to Code by Yourself (part 1).mp4 |
24.53MB |
| 1. How to Code by Yourself (part 1)-en_US.srt |
25.95KB |
| 1. How to Succeed in this Course (Long Version).mp4 |
18.31MB |
| 1. How to Succeed in this Course (Long Version)-en_US.srt |
14.00KB |
| 1. Introduction.mp4 |
34.24MB |
| 1. Introduction-en_US.srt |
4.03KB |
| 1. MDP Section Introduction.mp4 |
37.20MB |
| 1. MDP Section Introduction-en_US.srt |
8.02KB |
| 1. Monte Carlo Intro.mp4 |
47.59MB |
| 1. Monte Carlo Intro-en_US.srt |
12.12KB |
| 1. Section Introduction The Explore-Exploit Dilemma.mp4 |
51.99MB |
| 1. Section Introduction The Explore-Exploit Dilemma-en_US.srt |
12.99KB |
| 1. Temporal Difference Introduction.mp4 |
14.44MB |
| 1. Temporal Difference Introduction-en_US.srt |
5.04KB |
| 1. This Course vs. RL Book What's the Difference.mp4 |
38.21MB |
| 1. This Course vs. RL Book What's the Difference-en_US.srt |
9.89KB |
| 1. What is Reinforcement Learning.mp4 |
54.62MB |
| 1. What is Reinforcement Learning-en_US.srt |
10.51KB |
| 1. What is the Appendix.mp4 |
5.45MB |
| 1. What is the Appendix-en_US.srt |
3.58KB |
| 1. Windows-Focused Environment Setup 2018.mp4 |
186.38MB |
| 1. Windows-Focused Environment Setup 2018-en_US.srt |
19.29KB |
| 10 |
280.95KB |
| 10. Approximation Methods Exercise.mp4 |
17.53MB |
| 10. Approximation Methods Exercise-en_US.srt |
5.13KB |
| 10. Optimistic Initial Values Beginner's Exercise Prompt.mp4 |
13.77MB |
| 10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt |
2.82KB |
| 10. Policy Iteration in Code.mp4 |
56.38MB |
| 10. Policy Iteration in Code-en_US.srt |
10.39KB |
| 10. Stock Trading Project Discussion.mp4 |
15.78MB |
| 10. Stock Trading Project Discussion-en_US.srt |
4.19KB |
| 10. The Bellman Equation (pt 3).mp4 |
24.67MB |
| 10. The Bellman Equation (pt 3)-en_US.srt |
7.39KB |
| 100 |
201.06KB |
| 101 |
572.25KB |
| 102 |
741.53KB |
| 103 |
230.58KB |
| 104 |
268.75KB |
| 105 |
616.93KB |
| 106 |
987.43KB |
| 107 |
173.08KB |
| 11 |
180.48KB |
| 11. Approximation Methods Section Summary.mp4 |
21.75MB |
| 11. Approximation Methods Section Summary-en_US.srt |
3.85KB |
| 11. Bellman Examples.mp4 |
87.12MB |
| 11. Bellman Examples-en_US.srt |
26.62KB |
| 11. Optimistic Initial Values Code.mp4 |
24.57MB |
| 11. Optimistic Initial Values Code-en_US.srt |
4.99KB |
| 11. Policy Iteration in Windy Gridworld.mp4 |
51.41MB |
| 11. Policy Iteration in Windy Gridworld-en_US.srt |
10.57KB |
| 12 |
637.71KB |
| 12. Optimal Policy and Optimal Value Function (pt 1).mp4 |
56.06MB |
| 12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt |
11.04KB |
| 12. UCB1 Theory.mp4 |
55.53MB |
| 12. UCB1 Theory-en_US.srt |
19.19KB |
| 12. Value Iteration.mp4 |
35.27MB |
| 12. Value Iteration-en_US.srt |
9.31KB |
| 13 |
959.40KB |
| 13. Optimal Policy and Optimal Value Function (pt 2).mp4 |
15.72MB |
| 13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt |
4.91KB |
| 13. UCB1 Beginner's Exercise Prompt.mp4 |
12.74MB |
| 13. UCB1 Beginner's Exercise Prompt-en_US.srt |
2.65KB |
| 13. Value Iteration in Code.mp4 |
45.67MB |
| 13. Value Iteration in Code-en_US.srt |
8.51KB |
| 14 |
97.75KB |
| 14. Dynamic Programming Summary.mp4 |
25.11MB |
| 14. Dynamic Programming Summary-en_US.srt |
6.28KB |
| 14. MDP Summary.mp4 |
14.28MB |
| 14. MDP Summary-en_US.srt |
3.46KB |
| 14. UCB1 Code.mp4 |
20.66MB |
| 14. UCB1 Code-en_US.srt |
3.59KB |
| 15 |
485.31KB |
| 15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 |
55.90MB |
| 15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt |
16.13KB |
| 16 |
387.90KB |
| 16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 |
74.50MB |
| 16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt |
22.71KB |
| 17 |
10.86KB |
| 17. Thompson Sampling Beginner's Exercise Prompt.mp4 |
17.89MB |
| 17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt |
3.26KB |
| 18 |
64.50KB |
| 18. Thompson Sampling Code.mp4 |
32.83MB |
| 18. Thompson Sampling Code-en_US.srt |
5.43KB |
| 19 |
1015.38KB |
| 19. Thompson Sampling With Gaussian Reward Theory.mp4 |
48.51MB |
| 19. Thompson Sampling With Gaussian Reward Theory-en_US.srt |
14.41KB |
| 2 |
697.20KB |
| 2. Applications of the Explore-Exploit Dilemma.mp4 |
51.18MB |
| 2. Applications of the Explore-Exploit Dilemma-en_US.srt |
10.50KB |
| 2. BONUS Where to get discount coupons and FREE deep learning material.mp4 |
37.83MB |
| 2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt |
7.57KB |
| 2. Course Outline and Big Picture.mp4 |
39.68MB |
| 2. Course Outline and Big Picture-en_US.srt |
10.04KB |
| 2. From Bandits to Full Reinforcement Learning.mp4 |
41.19MB |
| 2. From Bandits to Full Reinforcement Learning-en_US.srt |
11.59KB |
| 2. Gridworld.mp4 |
53.99MB |
| 2. Gridworld-en_US.srt |
16.65KB |
| 2. How to Code by Yourself (part 2).mp4 |
14.80MB |
| 2. How to Code by Yourself (part 2)-en_US.srt |
15.79KB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.92MB |
| 2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt |
15.71KB |
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
38.95MB |
| 2. Iterative Policy Evaluation.mp4 |
60.82MB |
| 2. Iterative Policy Evaluation-en_US.srt |
20.37KB |
| 2. Linear Models for Reinforcement Learning.mp4 |
31.08MB |
| 2. Linear Models for Reinforcement Learning-en_US.srt |
10.97KB |
| 2. Monte Carlo Policy Evaluation.mp4 |
47.15MB |
| 2. Monte Carlo Policy Evaluation-en_US.srt |
14.07KB |
| 2. Stock Trading Project Section Introduction.mp4 |
26.76MB |
| 2. Stock Trading Project Section Introduction-en_US.srt |
6.58KB |
| 2. TD(0) Prediction.mp4 |
15.79MB |
| 2. TD(0) Prediction-en_US.srt |
6.64KB |
| 20 |
5.42KB |
| 20. Thompson Sampling With Gaussian Reward Code.mp4 |
43.43MB |
| 20. Thompson Sampling With Gaussian Reward Code-en_US.srt |
6.97KB |
| 21 |
361.41KB |
| 21. Why don't we just use a library.mp4 |
27.40MB |
| 21. Why don't we just use a library-en_US.srt |
7.32KB |
| 22 |
606.45KB |
| 22. Nonstationary Bandits.mp4 |
30.98MB |
| 22. Nonstationary Bandits-en_US.srt |
9.21KB |
| 23 |
840.72KB |
| 23. Bandit Summary, Real Data, and Online Learning.mp4 |
34.61MB |
| 23. Bandit Summary, Real Data, and Online Learning-en_US.srt |
8.76KB |
| 24 |
677.26KB |
| 24. (Optional) Alternative Bandit Designs.mp4 |
50.34MB |
| 24. (Optional) Alternative Bandit Designs-en_US.srt |
13.93KB |
| 25 |
282.56KB |
| 25. Suggestion Box.mp4 |
16.13MB |
| 25. Suggestion Box-en_US.srt |
4.50KB |
| 26 |
505.47KB |
| 27 |
419.86KB |
| 28 |
871.86KB |
| 29 |
71.52KB |
| 3 |
316.54KB |
| 3. Choosing Rewards.mp4 |
32.49MB |
| 3. Choosing Rewards-en_US.srt |
5.23KB |
| 3. Data and Environment.mp4 |
52.01MB |
| 3. Data and Environment-en_US.srt |
15.11KB |
| 3. Designing Your RL Program.mp4 |
22.34MB |
| 3. Designing Your RL Program-en_US.srt |
6.39KB |
| 3. Epsilon-Greedy Theory.mp4 |
28.30MB |
| 3. Epsilon-Greedy Theory-en_US.srt |
9.05KB |
| 3. External URLs.txt |
75B |
| 3. Feature Engineering.mp4 |
45.88MB |
| 3. Feature Engineering-en_US.srt |
13.90KB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 |
29.32MB |
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt |
15.45KB |
| 3. Monte Carlo Policy Evaluation in Code.mp4 |
51.65MB |
| 3. Monte Carlo Policy Evaluation in Code-en_US.srt |
10.17KB |
| 3. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.32MB |
| 3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt |
13.54KB |
| 3. TD(0) Prediction in Code.mp4 |
32.43MB |
| 3. TD(0) Prediction in Code-en_US.srt |
5.80KB |
| 3. Where to get the Code.mp4 |
22.72MB |
| 3. Where to get the Code-en_US.srt |
6.05KB |
| 30 |
178.11KB |
| 31 |
213.87KB |
| 32 |
127.63KB |
| 33 |
337.29KB |
| 34 |
98.73KB |
| 35 |
116.89KB |
| 36 |
14.30KB |
| 37 |
86.88KB |
| 38 |
181.04KB |
| 39 |
355.22KB |
| 4 |
511.05KB |
| 4. Approximation Methods for Prediction.mp4 |
34.34MB |
| 4. Approximation Methods for Prediction-en_US.srt |
12.06KB |
| 4. Calculating a Sample Mean (pt 1).mp4 |
23.13MB |
| 4. Calculating a Sample Mean (pt 1)-en_US.srt |
7.22KB |
| 4. Gridworld in Code.mp4 |
46.79MB |
| 4. Gridworld in Code-en_US.srt |
15.72KB |
| 4. How to Model Q for Q-Learning.mp4 |
44.89MB |
| 4. How to Model Q for Q-Learning-en_US.srt |
11.58KB |
| 4. How to Succeed in this Course.mp4 |
43.82MB |
| 4. How to Succeed in this Course-en_US.srt |
7.94KB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 |
37.62MB |
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt |
22.21KB |
| 4. Monte Carlo Control.mp4 |
35.61MB |
| 4. Monte Carlo Control-en_US.srt |
11.16KB |
| 4. Python 2 vs Python 3.mp4 |
7.83MB |
| 4. Python 2 vs Python 3-en_US.srt |
5.86KB |
| 4. SARSA.mp4 |
16.22MB |
| 4. SARSA-en_US.srt |
5.77KB |
| 4. The Markov Property.mp4 |
21.76MB |
| 4. The Markov Property-en_US.srt |
7.70KB |
| 40 |
588.54KB |
| 41 |
558.95KB |
| 42 |
579.25KB |
| 43 |
830.00KB |
| 44 |
320.22KB |
| 45 |
323.00KB |
| 46 |
512.09KB |
| 47 |
49.29KB |
| 48 |
465.42KB |
| 49 |
812.71KB |
| 5 |
583.75KB |
| 5. Approximation Methods for Prediction Code.mp4 |
62.29MB |
| 5. Approximation Methods for Prediction Code-en_US.srt |
10.20KB |
| 5. Design of the Program.mp4 |
23.31MB |
| 5. Design of the Program-en_US.srt |
8.19KB |
| 5. Epsilon-Greedy Beginner's Exercise Prompt.mp4 |
28.66MB |
| 5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt |
6.15KB |
| 5. Iterative Policy Evaluation in Code.mp4 |
68.43MB |
| 5. Iterative Policy Evaluation in Code-en_US.srt |
15.64KB |
| 5. Markov Decision Processes (MDPs).mp4 |
61.73MB |
| 5. Markov Decision Processes (MDPs)-en_US.srt |
18.85KB |
| 5. Monte Carlo Control in Code.mp4 |
64.41MB |
| 5. Monte Carlo Control in Code-en_US.srt |
10.74KB |
| 5. SARSA in Code.mp4 |
44.90MB |
| 5. SARSA in Code-en_US.srt |
7.40KB |
| 5. Warmup.mp4 |
62.60MB |
| 5. Warmup-en_US.srt |
18.14KB |
| 50 |
174.93KB |
| 51 |
393.10KB |
| 52 |
818.86KB |
| 53 |
399.73KB |
| 54 |
743.91KB |
| 55 |
340.87KB |
| 56 |
403.54KB |
| 57 |
678.06KB |
| 58 |
780.18KB |
| 59 |
866.79KB |
| 6 |
731.94KB |
| 6. Approximation Methods for Control.mp4 |
17.59MB |
| 6. Approximation Methods for Control-en_US.srt |
5.49KB |
| 6. Code pt 1.mp4 |
49.72MB |
| 6. Code pt 1-en_US.srt |
9.28KB |
| 6. Designing Your Bandit Program.mp4 |
24.51MB |
| 6. Designing Your Bandit Program-en_US.srt |
5.41KB |
| 6. Future Rewards.mp4 |
39.50MB |
| 6. Future Rewards-en_US.srt |
12.19KB |
| 6. Monte Carlo Control without Exploring Starts.mp4 |
23.40MB |
| 6. Monte Carlo Control without Exploring Starts-en_US.srt |
5.60KB |
| 6. Q Learning.mp4 |
19.82MB |
| 6. Q Learning-en_US.srt |
6.09KB |
| 6. Windy Gridworld in Code.mp4 |
41.45MB |
| 6. Windy Gridworld in Code-en_US.srt |
10.04KB |
| 60 |
285.86KB |
| 61 |
170.22KB |
| 62 |
522.20KB |
| 63 |
588.63KB |
| 64 |
943.83KB |
| 65 |
19.90KB |
| 66 |
697.20KB |
| 67 |
346.97KB |
| 68 |
712.95KB |
| 69 |
227.89KB |
| 7 |
602.76KB |
| 7. Approximation Methods for Control Code.mp4 |
77.69MB |
| 7. Approximation Methods for Control Code-en_US.srt |
10.55KB |
| 7. Code pt 2.mp4 |
65.29MB |
| 7. Code pt 2-en_US.srt |
11.31KB |
| 7. Epsilon-Greedy in Code.mp4 |
41.43MB |
| 7. Epsilon-Greedy in Code-en_US.srt |
8.30KB |
| 7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 |
46.93MB |
| 7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt |
9.34KB |
| 7. Monte Carlo Control without Exploring Starts in Code.mp4 |
40.69MB |
| 7. Monte Carlo Control without Exploring Starts in Code-en_US.srt |
6.91KB |
| 7. Q Learning in Code.mp4 |
38.55MB |
| 7. Q Learning in Code-en_US.srt |
5.83KB |
| 7. Value Functions.mp4 |
18.55MB |
| 7. Value Functions-en_US.srt |
6.38KB |
| 70 |
616.93KB |
| 71 |
98.39KB |
| 72 |
243.24KB |
| 73 |
318.56KB |
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909.64KB |
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333.58KB |
| 76 |
436.88KB |
| 77 |
479.06KB |
| 78 |
501.02KB |
| 79 |
489.15KB |
| 8 |
410.56KB |
| 8. CartPole.mp4 |
26.90MB |
| 8. CartPole-en_US.srt |
6.99KB |
| 8. Code pt 3.mp4 |
33.72MB |
| 8. Code pt 3-en_US.srt |
5.20KB |
| 8. Comparing Different Epsilons.mp4 |
43.65MB |
| 8. Comparing Different Epsilons-en_US.srt |
6.51KB |
| 8. Monte Carlo Summary.mp4 |
11.40MB |
| 8. Monte Carlo Summary-en_US.srt |
2.08KB |
| 8. Policy Improvement.mp4 |
43.99MB |
| 8. Policy Improvement-en_US.srt |
14.18KB |
| 8. TD Learning Section Summary.mp4 |
10.04MB |
| 8. TD Learning Section Summary-en_US.srt |
2.85KB |
| 8. The Bellman Equation (pt 1).mp4 |
27.78MB |
| 8. The Bellman Equation (pt 1)-en_US.srt |
10.68KB |
| 80 |
618.47KB |
| 81 |
705.04KB |
| 82 |
893.42KB |
| 83 |
287.10KB |
| 84 |
680.54KB |
| 85 |
946.98KB |
| 86 |
241.59KB |
| 87 |
253.06KB |
| 88 |
347.26KB |
| 89 |
184.74KB |
| 9 |
727.63KB |
| 9. CartPole Code.mp4 |
46.83MB |
| 9. CartPole Code-en_US.srt |
6.52KB |
| 9. Code pt 4.mp4 |
52.94MB |
| 9. Code pt 4-en_US.srt |
7.94KB |
| 9. Optimistic Initial Values Theory.mp4 |
23.52MB |
| 9. Optimistic Initial Values Theory-en_US.srt |
6.87KB |
| 9. Policy Iteration.mp4 |
34.15MB |
| 9. Policy Iteration-en_US.srt |
9.99KB |
| 9. The Bellman Equation (pt 2).mp4 |
26.69MB |
| 9. The Bellman Equation (pt 2)-en_US.srt |
8.24KB |
| 90 |
461.17KB |
| 91 |
709.11KB |
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110.23KB |
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422.77KB |
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477.43KB |
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799.89KB |
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894.89KB |
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216.54KB |
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224.82KB |
| 99 |
291.18KB |
| TutsNode.com.txt |
63B |