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

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

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种子哈希：0ce32fe1a70101bcfd8bd50d5a328e43f05f4f90
文件大小： 1.51G
已经下载：43次
下载速度：极快
收录时间：2022-03-04
最近下载：2024-12-28

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文件列表

  • 10. Appendix/2. Windows-Focused Environment Setup 2018.mp4 195.4 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 108.8 MB
  • 5. Markov Decision Proccesses/7. Bellman Examples.mp4 91.3 MB
  • 10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.mp4 57.3 MB
  • 3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp4 54.4 MB
  • 3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 53.7 MB
  • 10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.vtt 45.0 MB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.mp4 44.4 MB
  • 10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
  • 10. Appendix/11. What order should I take your courses in (part 2).mp4 39.4 MB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.mp4 38.9 MB
  • 1. Welcome/1. Introduction.mp4 35.9 MB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.mp4 32.5 MB
  • 10. Appendix/10. What order should I take your courses in (part 1).mp4 30.7 MB
  • 10. Appendix/4. How to Code by Yourself (part 1).mp4 25.7 MB
  • 3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp4 25.7 MB
  • 6. Dynamic Programming/3. Designing Your RL Program.mp4 23.4 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp4 20.7 MB
  • 5. Markov Decision Proccesses/5. Value Function Introduction.mp4 20.7 MB
  • 10. Appendix/6. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 10. Appendix/5. How to Code by Yourself (part 2).mp4 15.5 MB
  • 9. Approximation Methods/9. Course Summary and Next Steps.mp4 13.9 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4 13.3 MB
  • 6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp4 12.7 MB
  • 6. Dynamic Programming/2. Gridworld in Code.mp4 12.0 MB
  • 9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4 11.1 MB
  • 3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 11.1 MB
  • 7. Monte Carlo/6. Monte Carlo Control in Code.mp4 10.7 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp4 10.5 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4 10.3 MB
  • 1. Welcome/3. Strategy for Passing the Course.mp4 9.9 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4 9.9 MB
  • 7. Monte Carlo/5. Monte Carlo Control.mp4 9.7 MB
  • 6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp4 9.6 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp4 9.4 MB
  • 8. Temporal Difference Learning/5. SARSA in Code.mp4 9.2 MB
  • 7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 9.2 MB
  • 9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4 8.8 MB
  • 6. Dynamic Programming/11. Dynamic Programming Summary.mp4 8.7 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4 8.7 MB
  • 5. Markov Decision Proccesses/6. Value Functions.mp4 8.7 MB
  • 3. Return of the Multi-Armed Bandit/8. UCB1.mp4 8.6 MB
  • 8. Temporal Difference Learning/4. SARSA.mp4 8.6 MB
  • 7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4 8.4 MB
  • 3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp4 8.4 MB
  • 7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 8.3 MB
  • 10. Appendix/9. Python 2 vs Python 3.mp4 8.2 MB
  • 7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4 8.2 MB
  • 6. Dynamic Programming/7. Policy Iteration in Code.mp4 8.0 MB
  • 3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp4 7.9 MB
  • 5. Markov Decision Proccesses/2. The Markov Property.mp4 7.5 MB
  • 5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp4 7.0 MB
  • 9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp4 6.9 MB
  • 3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp4 6.8 MB
  • 9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 6.8 MB
  • 9. Approximation Methods/1. Approximation Intro.mp4 6.8 MB
  • 9. Approximation Methods/3. Features.mp4 6.5 MB
  • 6. Dynamic Programming/9. Value Iteration.mp4 6.5 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp4 6.4 MB
  • 8. Temporal Difference Learning/2. TD(0) Prediction.mp4 6.1 MB
  • 7. Monte Carlo/9. Monte Carlo Summary.mp4 6.0 MB
  • 10. Appendix/1. What is the Appendix.mp4 5.7 MB
  • 8. Temporal Difference Learning/7. Q Learning in Code.mp4 5.7 MB
  • 8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 5.6 MB
  • 5. Markov Decision Proccesses/4. Future Rewards.mp4 5.4 MB
  • 3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp4 5.4 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp4 5.3 MB
  • 7. Monte Carlo/1. Monte Carlo Intro.mp4 5.2 MB
  • 6. Dynamic Programming/10. Value Iteration in Code.mp4 5.1 MB
  • 8. Temporal Difference Learning/6. Q Learning.mp4 5.1 MB
  • 6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5.1 MB
  • 9. Approximation Methods/7. Semi-Gradient SARSA.mp4 4.9 MB
  • 7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp4 4.9 MB
  • 6. Dynamic Programming/5. Policy Improvement.mp4 4.8 MB
  • 1. Welcome/2. Where to get the Code.mp4 4.7 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp4 4.6 MB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp4 4.4 MB
  • 10. Appendix/12. Where to get discount coupons and FREE deep learning material.mp4 4.2 MB
  • 8. Temporal Difference Learning/8. TD Summary.mp4 4.1 MB
  • 5. Markov Decision Proccesses/1. Gridworld.mp4 3.5 MB
  • 5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp4 3.4 MB
  • 6. Dynamic Programming/6. Policy Iteration.mp4 3.3 MB
  • 9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp4 3.0 MB
  • 3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp4 2.9 MB
  • 8. Temporal Difference Learning/1. Temporal Difference Intro.mp4 2.9 MB
  • 5. Markov Decision Proccesses/9. MDP Summary.mp4 2.5 MB
  • 3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp4 2.3 MB
  • 10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 30.6 kB
  • 10. Appendix/4. How to Code by Yourself (part 1).vtt 28.0 kB
  • 5. Markov Decision Proccesses/7. Bellman Examples.vtt 26.4 kB
  • 10. Appendix/11. What order should I take your courses in (part 2).vtt 22.8 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.vtt 22.2 kB
  • 10. Appendix/2. Windows-Focused Environment Setup 2018.vtt 19.4 kB
  • 10. Appendix/5. How to Code by Yourself (part 2).vtt 17.1 kB
  • 10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 17.0 kB
  • 10. Appendix/10. What order should I take your courses in (part 1).vtt 15.5 kB
  • 5. Markov Decision Proccesses/5. Value Function Introduction.vtt 14.8 kB
  • 9. Approximation Methods/9. Course Summary and Next Steps.vtt 14.8 kB
  • 10. Appendix/6. How to Succeed in this Course (Long Version).vtt 14.0 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.vtt 13.7 kB
  • 10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 13.5 kB
  • 3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.vtt 11.3 kB
  • 5. Markov Decision Proccesses/6. Value Functions.vtt 11.3 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.vtt 11.1 kB
  • 1. Welcome/3. Strategy for Passing the Course.vtt 10.9 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.vtt 10.6 kB
  • 3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.vtt 10.5 kB
  • 6. Dynamic Programming/2. Gridworld in Code.vtt 10.2 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.vtt 10.2 kB
  • 7. Monte Carlo/2. Monte Carlo Policy Evaluation.vtt 10.1 kB
  • 7. Monte Carlo/5. Monte Carlo Control.vtt 9.6 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.vtt 9.6 kB
  • 6. Dynamic Programming/4. Iterative Policy Evaluation in Code.vtt 9.5 kB
  • 8. Temporal Difference Learning/4. SARSA.vtt 9.1 kB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.vtt 9.0 kB
  • 6. Dynamic Programming/11. Dynamic Programming Summary.vtt 8.8 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.vtt 8.6 kB
  • 5. Markov Decision Proccesses/2. The Markov Property.vtt 7.8 kB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.vtt 7.7 kB
  • 6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.vtt 7.7 kB
  • 3. Return of the Multi-Armed Bandit/8. UCB1.vtt 7.5 kB
  • 9. Approximation Methods/1. Approximation Intro.vtt 7.4 kB
  • 5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.vtt 7.3 kB
  • 3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.vtt 7.3 kB
  • 3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.vtt 7.3 kB
  • 9. Approximation Methods/2. Linear Models for Reinforcement Learning.vtt 6.9 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.vtt 6.7 kB
  • 7. Monte Carlo/9. Monte Carlo Summary.vtt 6.6 kB
  • 6. Dynamic Programming/9. Value Iteration.vtt 6.5 kB
  • 9. Approximation Methods/3. Features.vtt 6.5 kB
  • 6. Dynamic Programming/3. Designing Your RL Program.vtt 6.4 kB
  • 2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.vtt 6.2 kB
  • 10. Appendix/9. Python 2 vs Python 3.vtt 6.0 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.vtt 6.0 kB
  • 8. Temporal Difference Learning/2. TD(0) Prediction.vtt 6.0 kB
  • 9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.vtt 6.0 kB
  • 7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.vtt 5.7 kB
  • 6. Dynamic Programming/7. Policy Iteration in Code.vtt 5.7 kB
  • 3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.vtt 5.7 kB
  • 5. Markov Decision Proccesses/4. Future Rewards.vtt 5.6 kB
  • 7. Monte Carlo/1. Monte Carlo Intro.vtt 5.6 kB
  • 3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.vtt 5.5 kB
  • 8. Temporal Difference Learning/6. Q Learning.vtt 5.5 kB
  • 7. Monte Carlo/6. Monte Carlo Control in Code.vtt 5.5 kB
  • 8. Temporal Difference Learning/5. SARSA in Code.vtt 5.2 kB
  • 7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.vtt 5.2 kB
  • 9. Approximation Methods/7. Semi-Gradient SARSA.vtt 5.1 kB
  • 9. Approximation Methods/8. Semi-Gradient SARSA in Code.vtt 5.1 kB
  • 1. Welcome/2. Where to get the Code.vtt 5.0 kB
  • 6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.vtt 5.0 kB
  • 3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.vtt 5.0 kB
  • 7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.vtt 5.0 kB
  • 6. Dynamic Programming/5. Policy Improvement.vtt 4.8 kB
  • 5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.vtt 4.8 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.vtt 4.6 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.vtt 4.6 kB
  • 8. Temporal Difference Learning/8. TD Summary.vtt 4.4 kB
  • 4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.vtt 4.1 kB
  • 1. Welcome/1. Introduction.vtt 4.0 kB
  • 5. Markov Decision Proccesses/1. Gridworld.vtt 3.8 kB
  • 9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.vtt 3.8 kB
  • 8. Temporal Difference Learning/3. TD(0) Prediction in Code.vtt 3.7 kB
  • 10. Appendix/1. What is the Appendix.vtt 3.5 kB
  • 7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.vtt 3.4 kB
  • 10. Appendix/12. Where to get discount coupons and FREE deep learning material.vtt 3.4 kB
  • 6. Dynamic Programming/6. Policy Iteration.vtt 3.2 kB
  • 8. Temporal Difference Learning/7. Q Learning in Code.vtt 3.2 kB
  • 8. Temporal Difference Learning/1. Temporal Difference Intro.vtt 3.1 kB
  • 6. Dynamic Programming/10. Value Iteration in Code.vtt 3.1 kB
  • 3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.vtt 3.1 kB
  • 3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.vtt 3.0 kB
  • 5. Markov Decision Proccesses/9. MDP Summary.vtt 2.5 kB
  • 9. Approximation Methods/4. Monte Carlo Prediction with Approximation.vtt 2.2 kB
  • 3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.vtt 2.1 kB
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