FreeCourseSite com Udemy - Artificial Intelligence Reinforcement Learning in Python

1. 1. Introduction and Outline/1. Introduction and outline.mp410.1MB
2. 1. Introduction and Outline/2. What is Reinforcement Learning.mp421.95MB
3. 1. Introduction and Outline/3. Where to get the Code.mp44.46MB
4. 1. Introduction and Outline/4. Strategy for Passing the Course.mp49.48MB
5. 2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp46.48MB
6. 2. Return of the Multi-Armed Bandit/2. Epsilon-Greedy.mp42.79MB
7. 2. Return of the Multi-Armed Bandit/3. Updating a Sample Mean.mp42.17MB
8. 2. Return of the Multi-Armed Bandit/4. Comparing Different Epsilons.mp48.02MB
9. 2. Return of the Multi-Armed Bandit/5. Optimistic Initial Values.mp45.13MB
10. 2. Return of the Multi-Armed Bandit/6. UCB1.mp48.23MB
11. 2. Return of the Multi-Armed Bandit/7. Bayesian Thompson Sampling.mp451.85MB
12. 2. Return of the Multi-Armed Bandit/8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB
13. 2. Return of the Multi-Armed Bandit/9. Nonstationary Bandits.mp47.49MB
14. 3. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp46.11MB
15. 3. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp49.44MB
16. 3. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp48.32MB
17. 3. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp412.72MB
18. 3. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp44.23MB
19. 3. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4103.72MB
20. 3. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp45.04MB
21. 3. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp44.42MB
22. 3. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp49.79MB
23. 3. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp410.05MB
24. 3. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp49.01MB
25. 4. Markov Decision Proccesses/1. Gridworld.mp43.36MB
26. 4. Markov Decision Proccesses/2. The Markov Property.mp47.18MB
27. 4. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp46.64MB
28. 4. Markov Decision Proccesses/4. Future Rewards.mp45.17MB
29. 4. Markov Decision Proccesses/5. Value Function Introduction.mp419.72MB
30. 4. Markov Decision Proccesses/6. Value Functions.mp48.28MB
31. 4. Markov Decision Proccesses/7. Bellman Examples.mp487.12MB
32. 4. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp43.24MB
33. 4. Markov Decision Proccesses/9. MDP Summary.mp42.42MB
34. 5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.83MB
35. 5. Dynamic Programming/10. Dynamic Programming Summary.mp48.32MB
36. 5. Dynamic Programming/2. Gridworld in Code.mp411.46MB
37. 5. Dynamic Programming/3. Iterative Policy Evaluation in Code.mp412.07MB
38. 5. Dynamic Programming/4. Policy Improvement.mp44.54MB
39. 5. Dynamic Programming/5. Policy Iteration.mp43.14MB
40. 5. Dynamic Programming/6. Policy Iteration in Code.mp47.62MB
41. 5. Dynamic Programming/7. Policy Iteration in Windy Gridworld.mp49.1MB
42. 5. Dynamic Programming/8. Value Iteration.mp46.19MB
43. 5. Dynamic Programming/9. Value Iteration in Code.mp44.9MB
44. 6. Monte Carlo/1. Monte Carlo Intro.mp44.98MB
45. 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp48.76MB
46. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp47.92MB
47. 6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp47.81MB
48. 6. Monte Carlo/5. Monte Carlo Control.mp49.26MB
49. 6. Monte Carlo/6. Monte Carlo Control in Code.mp410.17MB
50. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp44.63MB
51. 6. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp48.06MB
52. 6. Monte Carlo/9. Monte Carlo Summary.mp45.71MB
53. 7. Temporal Difference Learning/1. Temporal Difference Intro.mp42.72MB
54. 7. Temporal Difference Learning/2. TD(0) Prediction.mp45.82MB
55. 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp45.32MB
56. 7. Temporal Difference Learning/4. SARSA.mp48.21MB
57. 7. Temporal Difference Learning/5. SARSA in Code.mp48.82MB
58. 7. Temporal Difference Learning/6. Q Learning.mp44.85MB
59. 7. Temporal Difference Learning/7. Q Learning in Code.mp45.42MB
60. 7. Temporal Difference Learning/8. TD Summary.mp43.94MB
61. 8. Approximation Methods/1. Approximation Intro.mp46.46MB
62. 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp46.47MB
63. 8. Approximation Methods/3. Features.mp46.25MB
64. 8. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp42.85MB
65. 8. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp46.57MB
66. 8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp48.35MB
67. 8. Approximation Methods/7. Semi-Gradient SARSA.mp44.7MB
68. 8. Approximation Methods/8. Semi-Gradient SARSA in Code.mp410.61MB
69. 8. Approximation Methods/9. Course Summary and Next Steps.mp413.24MB
70. 9. Appendix/1. What is the Appendix.mp45.45MB
71. 9. Appendix/10. What order should I take your courses in (part 1).mp429.32MB
72. 9. Appendix/11. What order should I take your courses in (part 2).mp437.62MB
73. 9. Appendix/12. Where to get discount coupons and FREE deep learning material.mp44.03MB
74. 9. Appendix/2. Windows-Focused Environment Setup 2018.mp4186.38MB
75. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
76. 9. Appendix/4. How to Code by Yourself (part 1).mp424.53MB
77. 9. Appendix/5. How to Code by Yourself (part 2).mp414.81MB
78. 9. Appendix/6. How to Succeed in this Course (Long Version).mp418.31MB
79. 9. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
80. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.33MB
81. 9. Appendix/9. Python 2 vs Python 3.mp47.83MB