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种子名称： [FreeCourseLab.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
文件类型： 视频
文件数目： 88个文件
文件大小： 1.46 GB
收录时间： 2019-6-7 06:29
已经下载： 3次
资源热度： 130
最近下载： 2024-11-15 04:42

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

1. Welcome/1. Introduction.mp434.25MB

1. Welcome/2. Where to get the Code.mp44.46MB

1. Welcome/3. Strategy for Passing the Course.mp49.47MB

10. Appendix/1. What is the Appendix.mp45.46MB

10. Appendix/10. What order should I take your courses in (part 1).mp429.32MB

10. Appendix/11. What order should I take your courses in (part 2).mp437.62MB

10. Appendix/12. Where to get discount coupons and FREE deep learning material.mp44.03MB

10. Appendix/2. Windows-Focused Environment Setup 2018.mp4186.39MB

10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB

10. Appendix/4. How to Code by Yourself (part 1).mp424.53MB

10. Appendix/5. How to Code by Yourself (part 2).mp414.8MB

10. Appendix/6. How to Succeed in this Course (Long Version).mp418.32MB

10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB

10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.32MB

10. Appendix/9. Python 2 vs Python 3.mp47.84MB

2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.mp454.62MB

2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.mp437.1MB

2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.mp430.98MB

2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.mp442.34MB

3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp46.48MB

3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.58MB

3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp47.49MB

3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp451.19MB

3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp42.79MB

3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp42.18MB

3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp424.52MB

3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp48.02MB

3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp45.13MB

3. Return of the Multi-Armed Bandit/8. UCB1.mp48.23MB

3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp451.85MB

4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp46.12MB

4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp49.44MB

4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp48.31MB

4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp419.78MB

4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp412.72MB

4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp44.23MB

4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4103.72MB

4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp45.04MB

4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp44.43MB

4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp49.79MB

4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp410.05MB

4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp49.01MB

5. Markov Decision Proccesses/1. Gridworld.mp43.36MB

5. Markov Decision Proccesses/2. The Markov Property.mp47.19MB

5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp46.64MB

5. Markov Decision Proccesses/4. Future Rewards.mp45.18MB

5. Markov Decision Proccesses/5. Value Function Introduction.mp419.73MB

5. Markov Decision Proccesses/6. Value Functions.mp48.28MB

5. Markov Decision Proccesses/7. Bellman Examples.mp487.12MB

5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp43.24MB

5. Markov Decision Proccesses/9. MDP Summary.mp42.42MB

6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.84MB

6. Dynamic Programming/10. Value Iteration in Code.mp44.89MB

6. Dynamic Programming/11. Dynamic Programming Summary.mp48.32MB

6. Dynamic Programming/2. Gridworld in Code.mp411.47MB

6. Dynamic Programming/3. Designing Your RL Program.mp422.35MB

6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp412.07MB

6. Dynamic Programming/5. Policy Improvement.mp44.53MB

6. Dynamic Programming/6. Policy Iteration.mp43.13MB

6. Dynamic Programming/7. Policy Iteration in Code.mp47.62MB

6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp49.11MB

6. Dynamic Programming/9. Value Iteration.mp46.19MB

7. Monte Carlo/1. Monte Carlo Intro.mp44.97MB

7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp48.75MB

7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp47.92MB

7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp47.82MB

7. Monte Carlo/5. Monte Carlo Control.mp49.26MB

7. Monte Carlo/6. Monte Carlo Control in Code.mp410.17MB

7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp44.63MB

7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp48.06MB

7. Monte Carlo/9. Monte Carlo Summary.mp45.72MB

8. Temporal Difference Learning/1. Temporal Difference Intro.mp42.73MB

8. Temporal Difference Learning/2. TD(0) Prediction.mp45.83MB

8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp45.33MB

8. Temporal Difference Learning/4. SARSA.mp48.21MB

8. Temporal Difference Learning/5. SARSA in Code.mp48.82MB

8. Temporal Difference Learning/6. Q Learning.mp44.85MB

8. Temporal Difference Learning/7. Q Learning in Code.mp45.43MB

8. Temporal Difference Learning/8. TD Summary.mp43.95MB

9. Approximation Methods/1. Approximation Intro.mp46.47MB

9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp46.47MB

9. Approximation Methods/3. Features.mp46.24MB

9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp42.85MB

9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp46.57MB

9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp48.36MB

9. Approximation Methods/7. Semi-Gradient SARSA.mp44.7MB

9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp410.61MB

9. Approximation Methods/9. Course Summary and Next Steps.mp413.25MB