GigaCourse Com Udemy - Artificial Intelligence - Reinforcement Learning in Python

1. 1. Welcome/1. Introduction.mp434.24MB
2. 1. Welcome/2. Course Outline and Big Picture.mp439.68MB
3. 1. Welcome/3. Where to get the Code.mp422.72MB
4. 1. Welcome/4. How to Succeed in this Course.mp443.82MB
5. 1. Welcome/5. Warmup.mp462.6MB
6. 10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead.mp483.78MB
7. 10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion.mp415.78MB
8. 10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction.mp426.76MB
9. 10. Stock Trading Project with Reinforcement Learning/3. Data and Environment.mp452.01MB
10. 10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning.mp444.89MB
11. 10. Stock Trading Project with Reinforcement Learning/5. Design of the Program.mp423.31MB
12. 10. Stock Trading Project with Reinforcement Learning/6. Code pt 1.mp449.72MB
13. 10. Stock Trading Project with Reinforcement Learning/7. Code pt 2.mp465.29MB
14. 10. Stock Trading Project with Reinforcement Learning/8. Code pt 3.mp433.72MB
15. 10. Stock Trading Project with Reinforcement Learning/9. Code pt 4.mp452.94MB
16. 11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4186.38MB
17. 11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
18. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp424.53MB
19. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp414.8MB
20. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp478.32MB
21. 12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp47.83MB
22. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp418.31MB
23. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
24. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp429.32MB
25. 13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp437.62MB
26. 14. Appendix FAQ Finale/1. What is the Appendix.mp45.45MB
27. 14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB
28. 2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.mp451.99MB
29. 2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.mp413.77MB
30. 2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.mp424.57MB
31. 2. Return of the Multi-Armed Bandit/12. UCB1 Theory.mp455.53MB
32. 2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp412.74MB
33. 2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp420.66MB
34. 2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp455.9MB
35. 2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp474.5MB
36. 2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.mp417.89MB
37. 2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.mp432.83MB
38. 2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp448.51MB
39. 2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp451.18MB
40. 2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.mp443.43MB
41. 2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.mp427.4MB
42. 2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp430.98MB
43. 2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp434.61MB
44. 2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.mp450.34MB
45. 2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp416.13MB
46. 2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.mp428.3MB
47. 2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).mp423.13MB
48. 2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.mp428.66MB
49. 2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.mp424.51MB
50. 2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp441.43MB
51. 2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.mp443.65MB
52. 2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.mp423.52MB
53. 3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp454.62MB
54. 3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning.mp441.19MB
55. 4. Markov Decision Proccesses/1. MDP Section Introduction.mp437.2MB
56. 4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).mp424.67MB
57. 4. Markov Decision Proccesses/11. Bellman Examples.mp487.12MB
58. 4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).mp456.06MB
59. 4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp415.72MB
60. 4. Markov Decision Proccesses/14. MDP Summary.mp414.28MB
61. 4. Markov Decision Proccesses/2. Gridworld.mp453.99MB
62. 4. Markov Decision Proccesses/3. Choosing Rewards.mp432.49MB
63. 4. Markov Decision Proccesses/4. The Markov Property.mp421.76MB
64. 4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp461.73MB
65. 4. Markov Decision Proccesses/6. Future Rewards.mp439.5MB
66. 4. Markov Decision Proccesses/7. Value Functions.mp418.55MB
67. 4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).mp427.78MB
68. 4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp426.69MB
69. 5. Dynamic Programming/1. Dynamic Programming Section Introduction.mp434.67MB
70. 5. Dynamic Programming/10. Policy Iteration in Code.mp456.38MB
71. 5. Dynamic Programming/11. Policy Iteration in Windy Gridworld.mp451.41MB
72. 5. Dynamic Programming/12. Value Iteration.mp435.27MB
73. 5. Dynamic Programming/13. Value Iteration in Code.mp445.67MB
74. 5. Dynamic Programming/14. Dynamic Programming Summary.mp425.11MB
75. 5. Dynamic Programming/2. Iterative Policy Evaluation.mp460.82MB
76. 5. Dynamic Programming/3. Designing Your RL Program.mp422.34MB
77. 5. Dynamic Programming/4. Gridworld in Code.mp446.79MB
78. 5. Dynamic Programming/5. Iterative Policy Evaluation in Code.mp468.43MB
79. 5. Dynamic Programming/6. Windy Gridworld in Code.mp441.45MB
80. 5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code.mp446.93MB
81. 5. Dynamic Programming/8. Policy Improvement.mp443.99MB
82. 5. Dynamic Programming/9. Policy Iteration.mp434.15MB
83. 6. Monte Carlo/1. Monte Carlo Intro.mp447.59MB
84. 6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp447.15MB
85. 6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp451.65MB
86. 6. Monte Carlo/4. Monte Carlo Control.mp435.61MB
87. 6. Monte Carlo/5. Monte Carlo Control in Code.mp464.41MB
88. 6. Monte Carlo/6. Monte Carlo Control without Exploring Starts.mp423.4MB
89. 6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code.mp440.69MB
90. 6. Monte Carlo/8. Monte Carlo Summary.mp411.4MB
91. 7. Temporal Difference Learning/1. Temporal Difference Introduction.mp414.44MB
92. 7. Temporal Difference Learning/2. TD(0) Prediction.mp415.79MB
93. 7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp432.43MB
94. 7. Temporal Difference Learning/4. SARSA.mp416.22MB
95. 7. Temporal Difference Learning/5. SARSA in Code.mp444.9MB
96. 7. Temporal Difference Learning/6. Q Learning.mp419.82MB
97. 7. Temporal Difference Learning/7. Q Learning in Code.mp438.55MB
98. 7. Temporal Difference Learning/8. TD Learning Section Summary.mp410.04MB
99. 8. Approximation Methods/1. Approximation Methods Section Introduction.mp422.08MB
100. 8. Approximation Methods/10. Approximation Methods Exercise.mp417.53MB
101. 8. Approximation Methods/11. Approximation Methods Section Summary.mp421.75MB
102. 8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp431.08MB
103. 8. Approximation Methods/3. Feature Engineering.mp445.88MB
104. 8. Approximation Methods/4. Approximation Methods for Prediction.mp434.34MB
105. 8. Approximation Methods/5. Approximation Methods for Prediction Code.mp462.29MB
106. 8. Approximation Methods/6. Approximation Methods for Control.mp417.59MB
107. 8. Approximation Methods/7. Approximation Methods for Control Code.mp477.69MB
108. 8. Approximation Methods/8. CartPole.mp426.9MB
109. 8. Approximation Methods/9. CartPole Code.mp446.83MB
110. 9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference.mp438.21MB