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[FreeCourseLab.com] Udemy - Artificial Intelligence Reinforcement Learning in Python

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视频 2019-6-7 06:29 2024-11-15 17:03 131 1.46 GB 88
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FreeCourseLab com Udemy - Artificial Intelligence Reinforcement Learning in Python

文件列表
  1. 1. Welcome/1. Introduction.mp434.25MB
  2. 1. Welcome/2. Where to get the Code.mp44.46MB
  3. 1. Welcome/3. Strategy for Passing the Course.mp49.47MB
  4. 10. Appendix/1. What is the Appendix.mp45.46MB
  5. 10. Appendix/10. What order should I take your courses in (part 1).mp429.32MB
  6. 10. Appendix/11. What order should I take your courses in (part 2).mp437.62MB
  7. 10. Appendix/12. Where to get discount coupons and FREE deep learning material.mp44.03MB
  8. 10. Appendix/2. Windows-Focused Environment Setup 2018.mp4186.39MB
  9. 10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
  10. 10. Appendix/4. How to Code by Yourself (part 1).mp424.53MB
  11. 10. Appendix/5. How to Code by Yourself (part 2).mp414.8MB
  12. 10. Appendix/6. How to Succeed in this Course (Long Version).mp418.32MB
  13. 10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB
  14. 10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.32MB
  15. 10. Appendix/9. Python 2 vs Python 3.mp47.84MB
  16. 2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.mp454.62MB
  17. 2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.mp437.1MB
  18. 2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.mp430.98MB
  19. 2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.mp442.34MB
  20. 3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp46.48MB
  21. 3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.58MB
  22. 3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp47.49MB
  23. 3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp451.19MB
  24. 3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp42.79MB
  25. 3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp42.18MB
  26. 3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp424.52MB
  27. 3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp48.02MB
  28. 3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp45.13MB
  29. 3. Return of the Multi-Armed Bandit/8. UCB1.mp48.23MB
  30. 3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp451.85MB
  31. 4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp46.12MB
  32. 4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp49.44MB
  33. 4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp48.31MB
  34. 4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp419.78MB
  35. 4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp412.72MB
  36. 4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp44.23MB
  37. 4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4103.72MB
  38. 4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp45.04MB
  39. 4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp44.43MB
  40. 4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp49.79MB
  41. 4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp410.05MB
  42. 4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp49.01MB
  43. 5. Markov Decision Proccesses/1. Gridworld.mp43.36MB
  44. 5. Markov Decision Proccesses/2. The Markov Property.mp47.19MB
  45. 5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp46.64MB
  46. 5. Markov Decision Proccesses/4. Future Rewards.mp45.18MB
  47. 5. Markov Decision Proccesses/5. Value Function Introduction.mp419.73MB
  48. 5. Markov Decision Proccesses/6. Value Functions.mp48.28MB
  49. 5. Markov Decision Proccesses/7. Bellman Examples.mp487.12MB
  50. 5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp43.24MB
  51. 5. Markov Decision Proccesses/9. MDP Summary.mp42.42MB
  52. 6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.84MB
  53. 6. Dynamic Programming/10. Value Iteration in Code.mp44.89MB
  54. 6. Dynamic Programming/11. Dynamic Programming Summary.mp48.32MB
  55. 6. Dynamic Programming/2. Gridworld in Code.mp411.47MB
  56. 6. Dynamic Programming/3. Designing Your RL Program.mp422.35MB
  57. 6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp412.07MB
  58. 6. Dynamic Programming/5. Policy Improvement.mp44.53MB
  59. 6. Dynamic Programming/6. Policy Iteration.mp43.13MB
  60. 6. Dynamic Programming/7. Policy Iteration in Code.mp47.62MB
  61. 6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp49.11MB
  62. 6. Dynamic Programming/9. Value Iteration.mp46.19MB
  63. 7. Monte Carlo/1. Monte Carlo Intro.mp44.97MB
  64. 7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp48.75MB
  65. 7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp47.92MB
  66. 7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp47.82MB
  67. 7. Monte Carlo/5. Monte Carlo Control.mp49.26MB
  68. 7. Monte Carlo/6. Monte Carlo Control in Code.mp410.17MB
  69. 7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp44.63MB
  70. 7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp48.06MB
  71. 7. Monte Carlo/9. Monte Carlo Summary.mp45.72MB
  72. 8. Temporal Difference Learning/1. Temporal Difference Intro.mp42.73MB
  73. 8. Temporal Difference Learning/2. TD(0) Prediction.mp45.83MB
  74. 8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp45.33MB
  75. 8. Temporal Difference Learning/4. SARSA.mp48.21MB
  76. 8. Temporal Difference Learning/5. SARSA in Code.mp48.82MB
  77. 8. Temporal Difference Learning/6. Q Learning.mp44.85MB
  78. 8. Temporal Difference Learning/7. Q Learning in Code.mp45.43MB
  79. 8. Temporal Difference Learning/8. TD Summary.mp43.95MB
  80. 9. Approximation Methods/1. Approximation Intro.mp46.47MB
  81. 9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp46.47MB
  82. 9. Approximation Methods/3. Features.mp46.24MB
  83. 9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp42.85MB
  84. 9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp46.57MB
  85. 9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp48.36MB
  86. 9. Approximation Methods/7. Semi-Gradient SARSA.mp44.7MB
  87. 9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp410.61MB
  88. 9. Approximation Methods/9. Course Summary and Next Steps.mp413.25MB
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