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

Creation Time	Nov. 6, 2023, 1:10 a.m.
Last Access Time	Oct. 23, 2024, 3:55 p.m.
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Keywords	FreeCourseSite Python Intelligence - Artificial Udemy Reinforcement Learning in com
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Total Files	224
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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
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1. Welcome/3. External URLs.txt 75 bytes
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[FreeCourseSite.com] Udemy - Artificial Intelligence - Reinforcement Learning in Python

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