种子简介

种子名称： [FreeCourseSite.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
文件类型： 视频
文件数目： 81个文件
文件大小： 1.19 GB
收录时间： 2020-12-5 00:08
已经下载： 3次
资源热度： 250
最近下载： 2024-11-28 05:43

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:2c2ece5a1762052507c7237eff15c94a55f65c72&dn=[FreeCourseSite.com] Udemy - Artificial Intelligence Reinforcement Learning in Python 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

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

1. Introduction and Outline/1. Introduction and outline.mp410.1MB

1. Introduction and Outline/2. What is Reinforcement Learning.mp421.95MB

1. Introduction and Outline/3. Where to get the Code.mp44.46MB

1. Introduction and Outline/4. Strategy for Passing the Course.mp49.48MB

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

2. Return of the Multi-Armed Bandit/2. Epsilon-Greedy.mp42.79MB

2. Return of the Multi-Armed Bandit/3. Updating a Sample Mean.mp42.17MB

2. Return of the Multi-Armed Bandit/4. Comparing Different Epsilons.mp48.02MB

2. Return of the Multi-Armed Bandit/5. Optimistic Initial Values.mp45.13MB

2. Return of the Multi-Armed Bandit/6. UCB1.mp48.23MB

2. Return of the Multi-Armed Bandit/7. Bayesian Thompson Sampling.mp451.85MB

2. Return of the Multi-Armed Bandit/8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB

2. Return of the Multi-Armed Bandit/9. Nonstationary Bandits.mp47.49MB

3. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp46.11MB

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

3. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp48.32MB

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

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

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

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

3. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp44.42MB

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

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

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

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

4. Markov Decision Proccesses/2. The Markov Property.mp47.18MB

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

4. Markov Decision Proccesses/4. Future Rewards.mp45.17MB

4. Markov Decision Proccesses/5. Value Function Introduction.mp419.72MB

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

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

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

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

5. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.83MB

5. Dynamic Programming/10. Dynamic Programming Summary.mp48.32MB

5. Dynamic Programming/2. Gridworld in Code.mp411.46MB

5. Dynamic Programming/3. Iterative Policy Evaluation in Code.mp412.07MB

5. Dynamic Programming/4. Policy Improvement.mp44.54MB

5. Dynamic Programming/5. Policy Iteration.mp43.14MB

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

5. Dynamic Programming/7. Policy Iteration in Windy Gridworld.mp49.1MB

5. Dynamic Programming/8. Value Iteration.mp46.19MB

5. Dynamic Programming/9. Value Iteration in Code.mp44.9MB

6. Monte Carlo/1. Monte Carlo Intro.mp44.98MB

6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp48.76MB

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

6. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp47.81MB

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

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

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

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

6. Monte Carlo/9. Monte Carlo Summary.mp45.71MB

7. Temporal Difference Learning/1. Temporal Difference Intro.mp42.72MB

7. Temporal Difference Learning/2. TD(0) Prediction.mp45.82MB

7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp45.32MB

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

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

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

7. Temporal Difference Learning/7. Q Learning in Code.mp45.42MB

7. Temporal Difference Learning/8. TD Summary.mp43.94MB

8. Approximation Methods/1. Approximation Intro.mp46.46MB

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

8. Approximation Methods/3. Features.mp46.25MB

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

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

8. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp48.35MB

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

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

8. Approximation Methods/9. Course Summary and Next Steps.mp413.24MB

9. Appendix/1. What is the Appendix.mp45.45MB

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

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

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

9. Appendix/2. Windows-Focused Environment Setup 2018.mp4186.38MB

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

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

9. Appendix/5. How to Code by Yourself (part 2).mp414.81MB

9. Appendix/6. How to Succeed in this Course (Long Version).mp418.31MB

9. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB

9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.33MB

9. Appendix/9. Python 2 vs Python 3.mp47.83MB