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

Creation Time	May 27, 2024, 1:10 a.m.
Last Access Time	Nov. 2, 2024, 1:24 p.m.
File Size	4.1 GB
Keywords	Reinforcement Python Intelligence - Artificial Udemy GigaCourse Learning in Com
Total Requests	46
Total Files	228
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018.mp4 186.4 MB
4. Markov Decision Proccesses/11. Bellman Examples.mp4 87.1 MB
10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead.mp4 83.8 MB
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.mp4 78.3 MB
8. Approximation Methods/7. Approximation Methods for Control Code.mp4 77.7 MB
2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4 74.5 MB
5. Dynamic Programming/5. Iterative Policy Evaluation in Code.mp4 68.4 MB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 2.mp4 65.3 MB
6. Monte Carlo/5. Monte Carlo Control in Code.mp4 64.4 MB
1. Welcome/5. Warmup.mp4 62.6 MB
8. Approximation Methods/5. Approximation Methods for Prediction Code.mp4 62.3 MB
4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs).mp4 61.7 MB
5. Dynamic Programming/2. Iterative Policy Evaluation.mp4 60.8 MB
5. Dynamic Programming/10. Policy Iteration in Code.mp4 56.4 MB
4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1).mp4 56.1 MB
2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4 55.9 MB
2. Return of the Multi-Armed Bandit/12. UCB1 Theory.mp4 55.5 MB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp4 54.6 MB
4. Markov Decision Proccesses/2. Gridworld.mp4 54.0 MB
10. Stock Trading Project with Reinforcement Learning/9. Code pt 4.mp4 52.9 MB
10. Stock Trading Project with Reinforcement Learning/3. Data and Environment.mp4 52.0 MB
2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma.mp4 52.0 MB
6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 51.6 MB
5. Dynamic Programming/11. Policy Iteration in Windy Gridworld.mp4 51.4 MB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 51.2 MB
2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs.mp4 50.3 MB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 1.mp4 49.7 MB
2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory.mp4 48.5 MB
6. Monte Carlo/1. Monte Carlo Intro.mp4 47.6 MB
6. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 47.1 MB
5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4 46.9 MB
8. Approximation Methods/9. CartPole Code.mp4 46.8 MB
5. Dynamic Programming/4. Gridworld in Code.mp4 46.8 MB
8. Approximation Methods/3. Feature Engineering.mp4 45.9 MB
5. Dynamic Programming/13. Value Iteration in Code.mp4 45.7 MB
7. Temporal Difference Learning/5. SARSA in Code.mp4 44.9 MB
10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning.mp4 44.9 MB
5. Dynamic Programming/8. Policy Improvement.mp4 44.0 MB
11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
1. Welcome/4. How to Succeed in this Course.mp4 43.8 MB
2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons.mp4 43.7 MB
2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code.mp4 43.4 MB
5. Dynamic Programming/6. Windy Gridworld in Code.mp4 41.5 MB
2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code.mp4 41.4 MB
3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning.mp4 41.2 MB
6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code.mp4 40.7 MB
1. Welcome/2. Course Outline and Big Picture.mp4 39.7 MB
4. Markov Decision Proccesses/6. Future Rewards.mp4 39.5 MB
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.mp4 39.0 MB
7. Temporal Difference Learning/7. Q Learning in Code.mp4 38.5 MB
9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference.mp4 38.2 MB
14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 37.6 MB
4. Markov Decision Proccesses/1. MDP Section Introduction.mp4 37.2 MB
6. Monte Carlo/4. Monte Carlo Control.mp4 35.6 MB
5. Dynamic Programming/12. Value Iteration.mp4 35.3 MB
5. Dynamic Programming/1. Dynamic Programming Section Introduction.mp4 34.7 MB
2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning.mp4 34.6 MB
8. Approximation Methods/4. Approximation Methods for Prediction.mp4 34.3 MB
1. Welcome/1. Introduction.mp4 34.2 MB
5. Dynamic Programming/9. Policy Iteration.mp4 34.2 MB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 3.mp4 33.7 MB
2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code.mp4 32.8 MB
4. Markov Decision Proccesses/3. Choosing Rewards.mp4 32.5 MB
7. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 32.4 MB
8. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 31.1 MB
2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits.mp4 31.0 MB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 29.3 MB
2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt.mp4 28.7 MB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory.mp4 28.3 MB
4. Markov Decision Proccesses/8. The Bellman Equation (pt 1).mp4 27.8 MB
2. Return of the Multi-Armed Bandit/21. Why don't we just use a library.mp4 27.4 MB
8. Approximation Methods/8. CartPole.mp4 26.9 MB
10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction.mp4 26.8 MB
4. Markov Decision Proccesses/9. The Bellman Equation (pt 2).mp4 26.7 MB
5. Dynamic Programming/14. Dynamic Programming Summary.mp4 25.1 MB
4. Markov Decision Proccesses/10. The Bellman Equation (pt 3).mp4 24.7 MB
2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code.mp4 24.6 MB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1).mp4 24.5 MB
2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program.mp4 24.5 MB
2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory.mp4 23.5 MB
6. Monte Carlo/6. Monte Carlo Control without Exploring Starts.mp4 23.4 MB
10. Stock Trading Project with Reinforcement Learning/5. Design of the Program.mp4 23.3 MB
2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1).mp4 23.1 MB
1. Welcome/3. Where to get the Code.mp4 22.7 MB
5. Dynamic Programming/3. Designing Your RL Program.mp4 22.3 MB
8. Approximation Methods/1. Approximation Methods Section Introduction.mp4 22.1 MB
4. Markov Decision Proccesses/4. The Markov Property.mp4 21.8 MB
8. Approximation Methods/11. Approximation Methods Section Summary.mp4 21.8 MB
2. Return of the Multi-Armed Bandit/14. UCB1 Code.mp4 20.7 MB
7. Temporal Difference Learning/6. Q Learning.mp4 19.8 MB
4. Markov Decision Proccesses/7. Value Functions.mp4 18.5 MB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 18.3 MB
2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt.mp4 17.9 MB
8. Approximation Methods/6. Approximation Methods for Control.mp4 17.6 MB
8. Approximation Methods/10. Approximation Methods Exercise.mp4 17.5 MB
7. Temporal Difference Learning/4. SARSA.mp4 16.2 MB
2. Return of the Multi-Armed Bandit/25. Suggestion Box.mp4 16.1 MB
7. Temporal Difference Learning/2. TD(0) Prediction.mp4 15.8 MB
10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion.mp4 15.8 MB
4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2).mp4 15.7 MB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2).mp4 14.8 MB
7. Temporal Difference Learning/1. Temporal Difference Introduction.mp4 14.4 MB
4. Markov Decision Proccesses/14. MDP Summary.mp4 14.3 MB
2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt.mp4 13.8 MB
2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt.mp4 12.7 MB
6. Monte Carlo/8. Monte Carlo Summary.mp4 11.4 MB
7. Temporal Difference Learning/8. TD Learning Section Summary.mp4 10.0 MB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3.mp4 7.8 MB
14. Appendix FAQ Finale/1. What is the Appendix.mp4 5.5 MB
4. Markov Decision Proccesses/11. Bellman Examples-en_US.srt 26.6 KB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code by Yourself (part 1)-en_US.srt 26.0 KB
2. Return of the Multi-Armed Bandit/16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt 22.7 KB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt 22.2 KB
5. Dynamic Programming/2. Iterative Policy Evaluation-en_US.srt 20.4 KB
10. Stock Trading Project with Reinforcement Learning/1. Beginners, halt! Stop here if you skipped ahead-en_US.srt 19.9 KB
11. Setting Up Your Environment (FAQ by Student Request)/1. Windows-Focused Environment Setup 2018-en_US.srt 19.3 KB
2. Return of the Multi-Armed Bandit/12. UCB1 Theory-en_US.srt 19.2 KB
4. Markov Decision Proccesses/5. Markov Decision Processes (MDPs)-en_US.srt 18.8 KB
1. Welcome/5. Warmup-en_US.srt 18.1 KB
4. Markov Decision Proccesses/2. Gridworld-en_US.srt 16.6 KB
2. Return of the Multi-Armed Bandit/15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt 16.1 KB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 2)-en_US.srt 15.8 KB
5. Dynamic Programming/4. Gridworld in Code-en_US.srt 15.7 KB
11. Setting Up Your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt 15.7 KB
5. Dynamic Programming/5. Iterative Policy Evaluation in Code-en_US.srt 15.6 KB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt 15.4 KB
10. Stock Trading Project with Reinforcement Learning/3. Data and Environment-en_US.srt 15.1 KB
2. Return of the Multi-Armed Bandit/19. Thompson Sampling With Gaussian Reward Theory-en_US.srt 14.4 KB
5. Dynamic Programming/8. Policy Improvement-en_US.srt 14.2 KB
6. Monte Carlo/2. Monte Carlo Policy Evaluation-en_US.srt 14.1 KB
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version)-en_US.srt 14.0 KB
2. Return of the Multi-Armed Bandit/24. (Optional) Alternative Bandit Designs-en_US.srt 13.9 KB
8. Approximation Methods/3. Feature Engineering-en_US.srt 13.9 KB
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-en_US.srt 13.5 KB
2. Return of the Multi-Armed Bandit/1. Section Introduction The Explore-Exploit Dilemma-en_US.srt 13.0 KB
4. Markov Decision Proccesses/6. Future Rewards-en_US.srt 12.2 KB
6. Monte Carlo/1. Monte Carlo Intro-en_US.srt 12.1 KB
8. Approximation Methods/4. Approximation Methods for Prediction-en_US.srt 12.1 KB
5. Dynamic Programming/1. Dynamic Programming Section Introduction-en_US.srt 11.9 KB
3. High Level Overview of Reinforcement Learning/2. From Bandits to Full Reinforcement Learning-en_US.srt 11.6 KB
10. Stock Trading Project with Reinforcement Learning/4. How to Model Q for Q-Learning-en_US.srt 11.6 KB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 2-en_US.srt 11.3 KB
6. Monte Carlo/4. Monte Carlo Control-en_US.srt 11.2 KB
4. Markov Decision Proccesses/12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt 11.0 KB
8. Approximation Methods/2. Linear Models for Reinforcement Learning-en_US.srt 11.0 KB
6. Monte Carlo/5. Monte Carlo Control in Code-en_US.srt 10.7 KB
4. Markov Decision Proccesses/8. The Bellman Equation (pt 1)-en_US.srt 10.7 KB
5. Dynamic Programming/11. Policy Iteration in Windy Gridworld-en_US.srt 10.6 KB
8. Approximation Methods/7. Approximation Methods for Control Code-en_US.srt 10.5 KB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning-en_US.srt 10.5 KB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma-en_US.srt 10.5 KB
5. Dynamic Programming/10. Policy Iteration in Code-en_US.srt 10.4 KB
8. Approximation Methods/5. Approximation Methods for Prediction Code-en_US.srt 10.2 KB
6. Monte Carlo/3. Monte Carlo Policy Evaluation in Code-en_US.srt 10.2 KB
1. Welcome/2. Course Outline and Big Picture-en_US.srt 10.0 KB
5. Dynamic Programming/6. Windy Gridworld in Code-en_US.srt 10.0 KB
5. Dynamic Programming/9. Policy Iteration-en_US.srt 10.0 KB
9. Interlude Common Beginner Questions/1. This Course vs. RL Book What's the Difference-en_US.srt 9.9 KB
5. Dynamic Programming/7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt 9.3 KB
5. Dynamic Programming/12. Value Iteration-en_US.srt 9.3 KB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 1-en_US.srt 9.3 KB
2. Return of the Multi-Armed Bandit/22. Nonstationary Bandits-en_US.srt 9.2 KB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy Theory-en_US.srt 9.1 KB
2. Return of the Multi-Armed Bandit/23. Bandit Summary, Real Data, and Online Learning-en_US.srt 8.8 KB
5. Dynamic Programming/13. Value Iteration in Code-en_US.srt 8.5 KB
2. Return of the Multi-Armed Bandit/7. Epsilon-Greedy in Code-en_US.srt 8.3 KB
4. Markov Decision Proccesses/9. The Bellman Equation (pt 2)-en_US.srt 8.2 KB
10. Stock Trading Project with Reinforcement Learning/5. Design of the Program-en_US.srt 8.2 KB
4. Markov Decision Proccesses/1. MDP Section Introduction-en_US.srt 8.0 KB
1. Welcome/4. How to Succeed in this Course-en_US.srt 7.9 KB
10. Stock Trading Project with Reinforcement Learning/9. Code pt 4-en_US.srt 7.9 KB
4. Markov Decision Proccesses/4. The Markov Property-en_US.srt 7.7 KB
14. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt 7.6 KB
7. Temporal Difference Learning/5. SARSA in Code-en_US.srt 7.4 KB
4. Markov Decision Proccesses/10. The Bellman Equation (pt 3)-en_US.srt 7.4 KB
2. Return of the Multi-Armed Bandit/21. Why don't we just use a library-en_US.srt 7.3 KB
2. Return of the Multi-Armed Bandit/4. Calculating a Sample Mean (pt 1)-en_US.srt 7.2 KB
8. Approximation Methods/8. CartPole-en_US.srt 7.0 KB
2. Return of the Multi-Armed Bandit/20. Thompson Sampling With Gaussian Reward Code-en_US.srt 7.0 KB
6. Monte Carlo/7. Monte Carlo Control without Exploring Starts in Code-en_US.srt 6.9 KB
2. Return of the Multi-Armed Bandit/9. Optimistic Initial Values Theory-en_US.srt 6.9 KB
7. Temporal Difference Learning/2. TD(0) Prediction-en_US.srt 6.6 KB
10. Stock Trading Project with Reinforcement Learning/2. Stock Trading Project Section Introduction-en_US.srt 6.6 KB
8. Approximation Methods/9. CartPole Code-en_US.srt 6.5 KB
2. Return of the Multi-Armed Bandit/8. Comparing Different Epsilons-en_US.srt 6.5 KB
5. Dynamic Programming/3. Designing Your RL Program-en_US.srt 6.4 KB
4. Markov Decision Proccesses/7. Value Functions-en_US.srt 6.4 KB
5. Dynamic Programming/14. Dynamic Programming Summary-en_US.srt 6.3 KB
2. Return of the Multi-Armed Bandit/5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt 6.2 KB
7. Temporal Difference Learning/6. Q Learning-en_US.srt 6.1 KB
1. Welcome/3. Where to get the Code-en_US.srt 6.1 KB
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Python 2 vs Python 3-en_US.srt 5.9 KB
7. Temporal Difference Learning/7. Q Learning in Code-en_US.srt 5.8 KB
7. Temporal Difference Learning/3. TD(0) Prediction in Code-en_US.srt 5.8 KB
7. Temporal Difference Learning/4. SARSA-en_US.srt 5.8 KB
6. Monte Carlo/6. Monte Carlo Control without Exploring Starts-en_US.srt 5.6 KB
8. Approximation Methods/1. Approximation Methods Section Introduction-en_US.srt 5.6 KB
8. Approximation Methods/6. Approximation Methods for Control-en_US.srt 5.5 KB
2. Return of the Multi-Armed Bandit/18. Thompson Sampling Code-en_US.srt 5.4 KB
2. Return of the Multi-Armed Bandit/6. Designing Your Bandit Program-en_US.srt 5.4 KB
4. Markov Decision Proccesses/3. Choosing Rewards-en_US.srt 5.2 KB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 3-en_US.srt 5.2 KB
8. Approximation Methods/10. Approximation Methods Exercise-en_US.srt 5.1 KB
7. Temporal Difference Learning/1. Temporal Difference Introduction-en_US.srt 5.0 KB
2. Return of the Multi-Armed Bandit/11. Optimistic Initial Values Code-en_US.srt 5.0 KB
4. Markov Decision Proccesses/13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt 4.9 KB
2. Return of the Multi-Armed Bandit/25. Suggestion Box-en_US.srt 4.5 KB
10. Stock Trading Project with Reinforcement Learning/10. Stock Trading Project Discussion-en_US.srt 4.2 KB
1. Welcome/1. Introduction-en_US.srt 4.0 KB
8. Approximation Methods/11. Approximation Methods Section Summary-en_US.srt 3.8 KB
2. Return of the Multi-Armed Bandit/14. UCB1 Code-en_US.srt 3.6 KB
14. Appendix FAQ Finale/1. What is the Appendix-en_US.srt 3.6 KB
4. Markov Decision Proccesses/14. MDP Summary-en_US.srt 3.5 KB
2. Return of the Multi-Armed Bandit/17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt 3.3 KB
7. Temporal Difference Learning/8. TD Learning Section Summary-en_US.srt 2.9 KB
2. Return of the Multi-Armed Bandit/10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt 2.8 KB
2. Return of the Multi-Armed Bandit/13. UCB1 Beginner's Exercise Prompt-en_US.srt 2.6 KB
6. Monte Carlo/8. Monte Carlo Summary-en_US.srt 2.1 KB
0. Websites you may like/[CourseClub.ME].url 122 bytes
10. Stock Trading Project with Reinforcement Learning/[CourseClub.Me].url 122 bytes
3. High Level Overview of Reinforcement Learning/[CourseClub.Me].url 122 bytes
[CourseClub.Me].url 122 bytes
1. Welcome/3. External URLs.txt 75 bytes
0. Websites you may like/[GigaCourse.Com].url 49 bytes
10. Stock Trading Project with Reinforcement Learning/[GigaCourse.Com].url 49 bytes
3. High Level Overview of Reinforcement Learning/[GigaCourse.Com].url 49 bytes
[GigaCourse.Com].url 49 bytes

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

File List

Download Torrent

Related Resources

Copyright Infringement