种子简介

种子名称： [DesireCourse.Net] Udemy - Artificial Intelligence Reinforcement Learning in Python
文件类型： 视频
文件数目： 98个文件
文件大小： 1.9 GB
收录时间： 2020-10-30 09:16
已经下载： 3次
资源热度： 167
最近下载： 2024-6-26 10:01

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:186fe32040da9247b2eeaf42415f6d0019895524&dn=[DesireCourse.Net] 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

[DesireCourse.Net] Udemy - Artificial Intelligence Reinforcement Learning in Python.torrent

1. Welcome/1. Introduction.mp434.24MB

1. Welcome/2. Where to get the Code.mp44.45MB

1. Welcome/3. Strategy for Passing the Course.mp49.48MB

1. Welcome/4. Course Outline.mp430.97MB

10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.mp426.77MB

10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.mp452.01MB

10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.mp444.89MB

10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.mp423.31MB

10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.mp449.72MB

10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.mp465.29MB

10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.mp433.72MB

10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.mp449.08MB

10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.mp415.78MB

11. Appendix FAQ/1. What is the Appendix.mp45.45MB

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

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

11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB

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

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

11. Appendix FAQ/4. How to Code by Yourself (part 1).mp424.54MB

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

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

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

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

11. Appendix FAQ/9. Python 2 vs Python 3.mp47.84MB

2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp46.47MB

2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB

2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp47.48MB

2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.mp433.92MB

2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp451.18MB

2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp42.78MB

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

2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp424.51MB

2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp48.01MB

2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp415.84MB

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

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

3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp454.62MB

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

3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.mp442.34MB

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.mp48.32MB

4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp419.77MB

4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp412.71MB

4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp44.22MB

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.mp44.42MB

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.mp47.18MB

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

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

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

5. Markov Decision Proccesses/6. Value Functions.mp48.29MB

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

5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp43.23MB

5. Markov Decision Proccesses/9. MDP Summary.mp45.67MB

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

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

6. Dynamic Programming/11. Dynamic Programming Summary.mp48.31MB

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

6. Dynamic Programming/3. Designing Your RL Program.mp422.34MB

6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp412.06MB

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

6. Dynamic Programming/6. Policy Iteration.mp43.14MB

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

6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp49.1MB

6. Dynamic Programming/9. Value Iteration.mp46.18MB

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.mp47.81MB

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.mp44.62MB

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

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

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

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

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

8. Temporal Difference Learning/4. SARSA.mp48.2MB

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

8. Temporal Difference Learning/6. Q Learning.mp44.84MB

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

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

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

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

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

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.mp413.24MB