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种子名称： [GigaCourse.Com] Udemy - Artificial Intelligence - Reinforcement Learning in Python
文件类型： 视频
文件数目： 110个文件
文件大小： 4.13 GB
收录时间： 2022-7-1 17:11
已经下载： 3次
资源热度： 201
最近下载： 2024-10-29 22:15

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[GigaCourse.Com] Udemy - Artificial Intelligence - Reinforcement Learning in Python.torrent

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

1. Welcome/2. Course Outline and Big Picture.mp439.68MB

1. Welcome/3. Where to get the Code.mp422.72MB

1. Welcome/4. How to Succeed in this Course.mp443.82MB

1. Welcome/5. Warmup.mp462.6MB

10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead.mp483.78MB

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

10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction.mp426.76MB

10. Stock Trading Project with Reinforcement Learning/3. Data and Environment.mp452.01MB

10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning.mp444.89MB

10. Stock Trading Project with Reinforcement Learning/5. Design of the Program.mp423.31MB

10. Stock Trading Project with Reinforcement Learning/6. Code pt 1.mp449.72MB

10. Stock Trading Project with Reinforcement Learning/7. Code pt 2.mp465.29MB

10. Stock Trading Project with Reinforcement Learning/8. Code pt 3.mp433.72MB

10. Stock Trading Project with Reinforcement Learning/9. Code pt 4.mp452.94MB

11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4186.38MB

11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB

12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp424.53MB

12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp414.8MB

12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp478.32MB

12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp47.83MB

13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp418.31MB

13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB

13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp429.32MB

13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp437.62MB

14. Appendix FAQ Finale/1. What is the Appendix.mp45.45MB

14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp437.83MB

2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.mp451.99MB

2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.mp413.77MB

2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.mp424.57MB

2. Return of the Multi-Armed Bandit/12. UCB1 Theory.mp455.53MB

2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp412.74MB

2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp420.66MB

2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp455.9MB

2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp474.5MB

2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.mp417.89MB

2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.mp432.83MB

2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp448.51MB

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

2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.mp443.43MB

2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.mp427.4MB

2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp430.98MB

2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp434.61MB

2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.mp450.34MB

2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp416.13MB

2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.mp428.3MB

2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).mp423.13MB

2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.mp428.66MB

2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.mp424.51MB

2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp441.43MB

2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.mp443.65MB

2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.mp423.52MB

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

3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning.mp441.19MB

4. Markov Decision Proccesses/1. MDP Section Introduction.mp437.2MB

4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).mp424.67MB

4. Markov Decision Proccesses/11. Bellman Examples.mp487.12MB

4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).mp456.06MB

4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp415.72MB

4. Markov Decision Proccesses/14. MDP Summary.mp414.28MB

4. Markov Decision Proccesses/2. Gridworld.mp453.99MB

4. Markov Decision Proccesses/3. Choosing Rewards.mp432.49MB

4. Markov Decision Proccesses/4. The Markov Property.mp421.76MB

4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp461.73MB

4. Markov Decision Proccesses/6. Future Rewards.mp439.5MB

4. Markov Decision Proccesses/7. Value Functions.mp418.55MB

4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).mp427.78MB

4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp426.69MB

5. Dynamic Programming/1. Dynamic Programming Section Introduction.mp434.67MB

5. Dynamic Programming/10. Policy Iteration in Code.mp456.38MB

5. Dynamic Programming/11. Policy Iteration in Windy Gridworld.mp451.41MB

5. Dynamic Programming/12. Value Iteration.mp435.27MB

5. Dynamic Programming/13. Value Iteration in Code.mp445.67MB

5. Dynamic Programming/14. Dynamic Programming Summary.mp425.11MB

5. Dynamic Programming/2. Iterative Policy Evaluation.mp460.82MB

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

5. Dynamic Programming/4. Gridworld in Code.mp446.79MB

5. Dynamic Programming/5. Iterative Policy Evaluation in Code.mp468.43MB

5. Dynamic Programming/6. Windy Gridworld in Code.mp441.45MB

5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code.mp446.93MB

5. Dynamic Programming/8. Policy Improvement.mp443.99MB

5. Dynamic Programming/9. Policy Iteration.mp434.15MB

6. Monte Carlo/1. Monte Carlo Intro.mp447.59MB

6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp447.15MB

6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp451.65MB

6. Monte Carlo/4. Monte Carlo Control.mp435.61MB

6. Monte Carlo/5. Monte Carlo Control in Code.mp464.41MB

6. Monte Carlo/6. Monte Carlo Control without Exploring Starts.mp423.4MB

6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code.mp440.69MB

6. Monte Carlo/8. Monte Carlo Summary.mp411.4MB

7. Temporal Difference Learning/1. Temporal Difference Introduction.mp414.44MB

7. Temporal Difference Learning/2. TD(0) Prediction.mp415.79MB

7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp432.43MB

7. Temporal Difference Learning/4. SARSA.mp416.22MB

7. Temporal Difference Learning/5. SARSA in Code.mp444.9MB

7. Temporal Difference Learning/6. Q Learning.mp419.82MB

7. Temporal Difference Learning/7. Q Learning in Code.mp438.55MB

7. Temporal Difference Learning/8. TD Learning Section Summary.mp410.04MB

8. Approximation Methods/1. Approximation Methods Section Introduction.mp422.08MB

8. Approximation Methods/10. Approximation Methods Exercise.mp417.53MB

8. Approximation Methods/11. Approximation Methods Section Summary.mp421.75MB

8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp431.08MB

8. Approximation Methods/3. Feature Engineering.mp445.88MB

8. Approximation Methods/4. Approximation Methods for Prediction.mp434.34MB

8. Approximation Methods/5. Approximation Methods for Prediction Code.mp462.29MB

8. Approximation Methods/6. Approximation Methods for Control.mp417.59MB

8. Approximation Methods/7. Approximation Methods for Control Code.mp477.69MB

8. Approximation Methods/8. CartPole.mp426.9MB

8. Approximation Methods/9. CartPole Code.mp446.83MB

9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference.mp438.21MB