DesireCourse Net Udemy - Artificial Intelligence Reinforcement Learning in Python

1. 1. Welcome/1. Introduction.mp434.24MB
2. 1. Welcome/2. Where to get the Code.mp44.45MB
3. 1. Welcome/3. Strategy for Passing the Course.mp49.48MB
4. 1. Welcome/4. Course Outline.mp430.97MB
5. 10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.mp426.77MB
6. 10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.mp452.01MB
7. 10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.mp444.89MB
8. 10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.mp423.31MB
9. 10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.mp449.72MB
10. 10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.mp465.29MB
11. 10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.mp433.72MB
12. 10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.mp449.08MB
13. 10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.mp415.78MB
14. 11. Appendix FAQ/1. What is the Appendix.mp45.45MB
15. 11. Appendix FAQ/10. What order should I take your courses in (part 1).mp429.32MB
16. 11. Appendix FAQ/11. What order should I take your courses in (part 2).mp437.62MB
17. 11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB
18. 11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4186.38MB
19. 11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
20. 11. Appendix FAQ/4. How to Code by Yourself (part 1).mp424.54MB
21. 11. Appendix FAQ/5. How to Code by Yourself (part 2).mp414.8MB
22. 11. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp418.31MB
23. 11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
24. 11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp478.32MB
25. 11. Appendix FAQ/9. Python 2 vs Python 3.mp47.84MB
26. 2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp46.47MB
27. 2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB
28. 2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp47.48MB
29. 2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.mp433.92MB
30. 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp451.18MB
31. 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp42.78MB
32. 2. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp42.18MB
33. 2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp424.51MB
34. 2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp48.01MB
35. 2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp415.84MB
36. 2. Return of the Multi-Armed Bandit/8. UCB1.mp48.23MB
37. 2. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp451.85MB
38. 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp454.62MB
39. 3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.mp437.1MB
40. 3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.mp442.34MB
41. 4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp46.12MB
42. 4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp49.44MB
43. 4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp48.32MB
44. 4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp419.77MB
45. 4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp412.71MB
46. 4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp44.22MB
47. 4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4103.72MB
48. 4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp45.04MB
49. 4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp44.42MB
50. 4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp49.79MB
51. 4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp410.05MB
52. 4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp49.01MB
53. 5. Markov Decision Proccesses/1. Gridworld.mp43.36MB
54. 5. Markov Decision Proccesses/2. The Markov Property.mp47.18MB
55. 5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp46.64MB
56. 5. Markov Decision Proccesses/4. Future Rewards.mp45.17MB
57. 5. Markov Decision Proccesses/5. Value Function Introduction.mp419.72MB
58. 5. Markov Decision Proccesses/6. Value Functions.mp48.29MB
59. 5. Markov Decision Proccesses/7. Bellman Examples.mp487.12MB
60. 5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp43.23MB
61. 5. Markov Decision Proccesses/9. MDP Summary.mp45.67MB
62. 6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.83MB
63. 6. Dynamic Programming/10. Value Iteration in Code.mp44.89MB
64. 6. Dynamic Programming/11. Dynamic Programming Summary.mp48.31MB
65. 6. Dynamic Programming/2. Gridworld in Code.mp411.46MB
66. 6. Dynamic Programming/3. Designing Your RL Program.mp422.34MB
67. 6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp412.06MB
68. 6. Dynamic Programming/5. Policy Improvement.mp44.53MB
69. 6. Dynamic Programming/6. Policy Iteration.mp43.14MB
70. 6. Dynamic Programming/7. Policy Iteration in Code.mp47.62MB
71. 6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp49.1MB
72. 6. Dynamic Programming/9. Value Iteration.mp46.18MB
73. 7. Monte Carlo/1. Monte Carlo Intro.mp44.97MB
74. 7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp48.75MB
75. 7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp47.92MB
76. 7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp47.81MB
77. 7. Monte Carlo/5. Monte Carlo Control.mp49.26MB
78. 7. Monte Carlo/6. Monte Carlo Control in Code.mp410.17MB
79. 7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp44.62MB
80. 7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp48.06MB
81. 7. Monte Carlo/9. Monte Carlo Summary.mp45.71MB
82. 8. Temporal Difference Learning/1. Temporal Difference Intro.mp42.73MB
83. 8. Temporal Difference Learning/2. TD(0) Prediction.mp45.82MB
84. 8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp45.32MB
85. 8. Temporal Difference Learning/4. SARSA.mp48.2MB
86. 8. Temporal Difference Learning/5. SARSA in Code.mp48.82MB
87. 8. Temporal Difference Learning/6. Q Learning.mp44.84MB
88. 8. Temporal Difference Learning/7. Q Learning in Code.mp45.42MB
89. 8. Temporal Difference Learning/8. TD Summary.mp43.94MB
90. 9. Approximation Methods/1. Approximation Intro.mp46.47MB
91. 9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp46.47MB
92. 9. Approximation Methods/3. Features.mp46.25MB
93. 9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp42.85MB
94. 9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp46.57MB
95. 9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp48.36MB
96. 9. Approximation Methods/7. Semi-Gradient SARSA.mp44.7MB
97. 9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp410.61MB
98. 9. Approximation Methods/9. Course Summary and Next Steps.mp413.24MB