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artificial-intelligence-reinforcement-learning-in-python

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文件列表

  1. 09 Appendix/068 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 44MB
  2. 03 Build an Intelligent Tic-Tac-Toe Agent/017 The Value Function and Your First Reinforcement Learning Algorithm.mp4 26MB
  3. 01 Introduction and Outline/002 What is Reinforcement Learning.mp4 22MB
  4. 02 Return of the Multi-Armed Bandit/011 Bayesian Thompson Sampling.mp4 15MB
  5. 08 Approximation Methods/067 Course Summary and Next Steps.mp4 13MB
  6. 03 Build an Intelligent Tic-Tac-Toe Agent/015 Components of a Reinforcement Learning System.mp4 13MB
  7. 05 Dynamic Programming/034 Iterative Policy Evaluation in Code.mp4 12MB
  8. 05 Dynamic Programming/033 Gridworld in Code.mp4 11MB
  9. 08 Approximation Methods/066 Semi-Gradient SARSA in Code.mp4 11MB
  10. 02 Return of the Multi-Armed Bandit/012 Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 11MB
  11. 06 Monte Carlo/047 Monte Carlo Control in Code.mp4 10MB
  12. 01 Introduction and Outline/001 Introduction and outline.mp4 10MB
  13. 03 Build an Intelligent Tic-Tac-Toe Agent/021 Tic Tac Toe Code The Environment.mp4 10MB
  14. 03 Build an Intelligent Tic-Tac-Toe Agent/020 Tic Tac Toe Code Enumerating States Recursively.mp4 10MB
  15. 01 Introduction and Outline/004 Strategy for Passing the Course.mp4 9MB
  16. 03 Build an Intelligent Tic-Tac-Toe Agent/023 Tic Tac Toe Code Main Loop and Demo.mp4 9MB
  17. 06 Monte Carlo/046 Monte Carlo Control.mp4 9MB
  18. 05 Dynamic Programming/038 Policy Iteration in Windy Gridworld.mp4 9MB
  19. 03 Build an Intelligent Tic-Tac-Toe Agent/022 Tic Tac Toe Code The Agent.mp4 9MB
  20. 07 Temporal Difference Learning/055 SARSA in Code.mp4 9MB
  21. 06 Monte Carlo/043 Monte Carlo Policy Evaluation.mp4 9MB
  22. 08 Approximation Methods/064 TD0 Semi-Gradient Prediction.mp4 8MB
  23. 05 Dynamic Programming/041 Dynamic Programming Summary.mp4 8MB
  24. 03 Build an Intelligent Tic-Tac-Toe Agent/024 Tic Tac Toe Summary.mp4 8MB
  25. 02 Return of the Multi-Armed Bandit/010 UCB1.mp4 8MB
  26. 07 Temporal Difference Learning/054 SARSA.mp4 8MB
  27. 06 Monte Carlo/049 Monte Carlo Control without Exploring Starts in Code.mp4 8MB
  28. 02 Return of the Multi-Armed Bandit/008 Comparing Different Epsilons.mp4 8MB
  29. 06 Monte Carlo/044 Monte Carlo Policy Evaluation in Code.mp4 8MB
  30. 06 Monte Carlo/045 Policy Evaluation in Windy Gridworld.mp4 8MB
  31. 05 Dynamic Programming/037 Policy Iteration in Code.mp4 8MB
  32. 02 Return of the Multi-Armed Bandit/013 Nonstationary Bandits.mp4 7MB
  33. 04 Markov Decision Proccesses/026 The Markov Property.mp4 7MB
  34. 04 Markov Decision Proccesses/029 Value Functions.mp4 7MB
  35. 04 Markov Decision Proccesses/027 Defining and Formalizing the MDP.mp4 7MB
  36. 08 Approximation Methods/063 Monte Carlo Prediction with Approximation in Code.mp4 7MB
  37. 02 Return of the Multi-Armed Bandit/005 Problem Setup and The Explore-Exploit Dilemma.mp4 6MB
  38. 08 Approximation Methods/060 Linear Models for Reinforcement Learning.mp4 6MB
  39. 08 Approximation Methods/059 Approximation Intro.mp4 6MB
  40. 04 Markov Decision Proccesses/030 Optimal Policy and Optimal Value Function.mp4 6MB
  41. 08 Approximation Methods/061 Features.mp4 6MB
  42. 05 Dynamic Programming/039 Value Iteration.mp4 6MB
  43. 03 Build an Intelligent Tic-Tac-Toe Agent/014 Naive Solution to Tic-Tac-Toe.mp4 6MB
  44. 07 Temporal Difference Learning/052 TD0 Prediction.mp4 6MB
  45. 06 Monte Carlo/050 Monte Carlo Summary.mp4 6MB
  46. 07 Temporal Difference Learning/057 Q Learning in Code.mp4 5MB
  47. 07 Temporal Difference Learning/053 TD0 Prediction in Code.mp4 5MB
  48. 04 Markov Decision Proccesses/028 Future Rewards.mp4 5MB
  49. 02 Return of the Multi-Armed Bandit/009 Optimistic Initial Values.mp4 5MB
  50. 03 Build an Intelligent Tic-Tac-Toe Agent/018 Tic Tac Toe Code Outline.mp4 5MB
  51. 06 Monte Carlo/042 Monte Carlo Intro.mp4 5MB
  52. 05 Dynamic Programming/040 Value Iteration in Code.mp4 5MB
  53. 07 Temporal Difference Learning/056 Q Learning.mp4 5MB
  54. 05 Dynamic Programming/032 Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5MB
  55. 08 Approximation Methods/065 Semi-Gradient SARSA.mp4 5MB
  56. 06 Monte Carlo/048 Monte Carlo Control without Exploring Starts.mp4 5MB
  57. 05 Dynamic Programming/035 Policy Improvement.mp4 5MB
  58. 01 Introduction and Outline/003 Where to get the Code.mp4 4MB
  59. 03 Build an Intelligent Tic-Tac-Toe Agent/019 Tic Tac Toe Code Representing States.mp4 4MB
  60. 03 Build an Intelligent Tic-Tac-Toe Agent/016 Notes on Assigning Rewards.mp4 4MB
  61. 09 Appendix/069 Where to get discount coupons and FREE deep learning material.mp4 4MB
  62. 07 Temporal Difference Learning/058 TD Summary.mp4 4MB
  63. 04 Markov Decision Proccesses/025 Gridworld.mp4 3MB
  64. 05 Dynamic Programming/036 Policy Iteration.mp4 3MB
  65. 08 Approximation Methods/062 Monte Carlo Prediction with Approximation.mp4 3MB
  66. 02 Return of the Multi-Armed Bandit/006 Epsilon-Greedy.mp4 3MB
  67. 07 Temporal Difference Learning/051 Temporal Difference Intro.mp4 3MB
  68. 04 Markov Decision Proccesses/031 MDP Summary.mp4 2MB
  69. 02 Return of the Multi-Armed Bandit/007 Updating a Sample Mean.mp4 2MB