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Artificial Intelligence Reinforcement Learning in Python
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[TutsNode.com] - Artificial Intelligence Reinforcement Learning in Python (Size: 4.18 GB) (Files: 330)
[TutsNode.com] - Artificial Intelligence Reinforcement Learning in Python
11. Setting Up Your Environment (FAQ by Student Request)
1. Windows-Focused Environment Setup 2018.mp4
1. Windows-Focused Environment Setup 2018-en_US.srt
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srt
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
4. Markov Decision Proccesses
11. Bellman Examples-en_US.srt
5. Markov Decision Processes (MDPs)-en_US.srt
2. Gridworld-en_US.srt
6. Future Rewards-en_US.srt
12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srt
8. The Bellman Equation (pt 1)-en_US.srt
10. The Bellman Equation (pt 3)-en_US.srt
11. Bellman Examples.mp4
9. The Bellman Equation (pt 2)-en_US.srt
1. MDP Section Introduction-en_US.srt
4. The Markov Property-en_US.srt
7. Value Functions-en_US.srt
3. Choosing Rewards-en_US.srt
13. Optimal Policy and Optimal Value Function (pt 2)-en_US.srt
14. MDP Summary-en_US.srt
5. Markov Decision Processes (MDPs).mp4
12. Optimal Policy and Optimal Value Function (pt 1).mp4
2. Gridworld.mp4
6. Future Rewards.mp4
1. MDP Section Introduction.mp4
3. Choosing Rewards.mp4
8. The Bellman Equation (pt 1).mp4
9. The Bellman Equation (pt 2).mp4
10. The Bellman Equation (pt 3).mp4
4. The Markov Property.mp4
7. Value Functions.mp4
13. Optimal Policy and Optimal Value Function (pt 2).mp4
14. MDP Summary.mp4
12. Extra Help With Python Coding for Beginners (FAQ by Student Request)
1. How to Code by Yourself (part 1)-en_US.srt
2. How to Code by Yourself (part 2)-en_US.srt
3. Proof that using Jupyter Notebook is the same as not using it-en_US.srt
4. Python 2 vs Python 3-en_US.srt
3. Proof that using Jupyter Notebook is the same as not using it.mp4
1. How to Code by Yourself (part 1).mp4
2. How to Code by Yourself (part 2).mp4
4. Python 2 vs Python 3.mp4
2. Return of the Multi-Armed Bandit
16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srt
12. UCB1 Theory-en_US.srt
15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srt
19. Thompson Sampling With Gaussian Reward Theory-en_US.srt
24. (Optional) Alternative Bandit Designs-en_US.srt
1. Section Introduction The Explore-Exploit Dilemma-en_US.srt
10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srt
13. UCB1 Beginner's Exercise Prompt-en_US.srt
2. Applications of the Explore-Exploit Dilemma-en_US.srt
22. Nonstationary Bandits-en_US.srt
3. Epsilon-Greedy Theory-en_US.srt
23. Bandit Summary, Real Data, and Online Learning-en_US.srt
7. Epsilon-Greedy in Code-en_US.srt
21. Why don't we just use a library-en_US.srt
4. Calculating a Sample Mean (pt 1)-en_US.srt
20. Thompson Sampling With Gaussian Reward Code-en_US.srt
9. Optimistic Initial Values Theory-en_US.srt
8. Comparing Different Epsilons-en_US.srt
5. Epsilon-Greedy Beginner's Exercise Prompt-en_US.srt
18. Thompson Sampling Code-en_US.srt
6. Designing Your Bandit Program-en_US.srt
11. Optimistic Initial Values Code-en_US.srt
25. Suggestion Box-en_US.srt
14. UCB1 Code-en_US.srt
17. Thompson Sampling Beginner's Exercise Prompt-en_US.srt
16. Bayesian Bandits Thompson Sampling Theory (pt 2).mp4
15. Bayesian Bandits Thompson Sampling Theory (pt 1).mp4
12. UCB1 Theory.mp4
1. Section Introduction The Explore-Exploit Dilemma.mp4
2. Applications of the Explore-Exploit Dilemma.mp4
24. (Optional) Alternative Bandit Designs.mp4
19. Thompson Sampling With Gaussian Reward Theory.mp4
8. Comparing Different Epsilons.mp4
20. Thompson Sampling With Gaussian Reward Code.mp4
7. Epsilon-Greedy in Code.mp4
23. Bandit Summary, Real Data, and Online Learning.mp4
18. Thompson Sampling Code.mp4
22. Nonstationary Bandits.mp4
5. Epsilon-Greedy Beginner's Exercise Prompt.mp4
3. Epsilon-Greedy Theory.mp4
21. Why don't we just use a library.mp4
11. Optimistic Initial Values Code.mp4
6. Designing Your Bandit Program.mp4
9. Optimistic Initial Values Theory.mp4
4. Calculating a Sample Mean (pt 1).mp4
14. UCB1 Code.mp4
17. Thompson Sampling Beginner's Exercise Prompt.mp4
25. Suggestion Box.mp4
10. Optimistic Initial Values Beginner's Exercise Prompt.mp4
13. UCB1 Beginner's Exercise Prompt.mp4
13. Effective Learning Strategies for Machine Learning (FAQ by Student Request)
4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srt
3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srt
1. How to Succeed in this Course (Long Version)-en_US.srt
2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4
3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4
1. How to Succeed in this Course (Long Version).mp4
1. Welcome
3. External URLs.txt
5. Warmup-en_US.srt
2. Course Outline and Big Picture-en_US.srt
4. How to Succeed in this Course-en_US.srt
3. Where to get the Code-en_US.srt
1. Introduction-en_US.srt
5. Warmup.mp4
4. How to Succeed in this Course.mp4
2. Course Outline and Big Picture.mp4
1. Introduction.mp4
3. Where to get the Code.mp4
5. Dynamic Programming
2. Iterative Policy Evaluation-en_US.srt
4. Gridworld in Code-en_US.srt
5. Iterative Policy Evaluation in Code-en_US.srt
8. Policy Improvement-en_US.srt
1. Dynamic Programming Section Introduction-en_US.srt
11. Policy Iteration in Windy Gridworld-en_US.srt
10. Policy Iteration in Code-en_US.srt
6. Windy Gridworld in Code-en_US.srt
9. Policy Iteration-en_US.srt
7. Iterative Policy Evaluation for Windy Gridworld in Code-en_US.srt
12. Value Iteration-en_US.srt
13. Value Iteration in Code-en_US.srt
3. Designing Your RL Program-en_US.srt
14. Dynamic Programming Summary-en_US.srt
5. Iterative Policy Evaluation in Code.mp4
2. Iterative Policy Evaluation.mp4
10. Policy Iteration in Code.mp4
11. Policy Iteration in Windy Gridworld.mp4
7. Iterative Policy Evaluation for Windy Gridworld in Code.mp4
4. Gridworld in Code.mp4
13. Value Iteration in Code.mp4
8. Policy Improvement.mp4
6. Windy Gridworld in Code.mp4
12. Value Iteration.mp4
1. Dynamic Programming Section Introduction.mp4
9. Policy Iteration.mp4
14. Dynamic Programming Summary.mp4
3. Designing Your RL Program.mp4
10. Stock Trading Project with Reinforcement Learning
1. Beginners, halt! Stop here if you skipped ahead-en_US.srt
3. Data and Environment-en_US.srt
4. How to Model Q for Q-Learning-en_US.srt
7. Code pt 2-en_US.srt
6. Code pt 1-en_US.srt
5. Design of the Program-en_US.srt
9. Code pt 4-en_US.srt
2. Stock Trading Project Section Introduction-en_US.srt
8. Code pt 3-en_US.srt
10. Stock Trading Project Discussion-en_US.srt
1. Beginners, halt! Stop here if you skipped ahead.mp4
7. Code pt 2.mp4
9. Code pt 4.mp4
3. Data and Environment.mp4
6. Code pt 1.mp4
4. How to Model Q for Q-Learning.mp4
8. Code pt 3.mp4
2. Stock Trading Project Section Introduction.mp4
5. Design of the Program.mp4
10. Stock Trading Project Discussion.mp4
6. Monte Carlo
2. Monte Carlo Policy Evaluation-en_US.srt
1. Monte Carlo Intro-en_US.srt
4. Monte Carlo Control-en_US.srt
5. Monte Carlo Control in Code-en_US.srt
3. Monte Carlo Policy Evaluation in Code-en_US.srt
7. Monte Carlo Control without Exploring Starts in Code-en_US.srt
6. Monte Carlo Control without Exploring Starts-en_US.srt
8. Monte Carlo Summary-en_US.srt
5. Monte Carlo Control in Code.mp4
3. Monte Carlo Policy Evaluation in Code.mp4
1. Monte Carlo Intro.mp4
2. Monte Carlo Policy Evaluation.mp4
7. Monte Carlo Control without Exploring Starts in Code.mp4
4. Monte Carlo Control.mp4
6. Monte Carlo Control without Exploring Starts.mp4
8. Monte Carlo Summary.mp4
8. Approximation Methods
3. Feature Engineering-en_US.srt
4. Approximation Methods for Prediction-en_US.srt
2. Linear Models for Reinforcement Learning-en_US.srt
7. Approximation Methods for Control Code-en_US.srt
5. Approximation Methods for Prediction Code-en_US.srt
8. CartPole-en_US.srt
9. CartPole Code-en_US.srt
1. Approximation Methods Section Introduction-en_US.srt
6. Approximation Methods for Control-en_US.srt
10. Approximation Methods Exercise-en_US.srt
11. Approximation Methods Section Summary-en_US.srt
7. Approximation Methods for Control Code.mp4
5. Approximation Methods for Prediction Code.mp4
9. CartPole Code.mp4
3. Feature Engineering.mp4
4. Approximation Methods for Prediction.mp4
2. Linear Models for Reinforcement Learning.mp4
8. CartPole.mp4
1. Approximation Methods Section Introduction.mp4
11. Approximation Methods Section Summary.mp4
6. Approximation Methods for Control.mp4
10. Approximation Methods Exercise.mp4
3. High Level Overview of Reinforcement Learning
2. From Bandits to Full Reinforcement Learning-en_US.srt
1. What is Reinforcement Learning-en_US.srt
1. What is Reinforcement Learning.mp4
2. From Bandits to Full Reinforcement Learning.mp4
9. Interlude Common Beginner Questions
1. This Course vs. RL Book What's the Difference-en_US.srt
1. This Course vs. RL Book What's the Difference.mp4
14. Appendix FAQ Finale
2. BONUS Where to get discount coupons and FREE deep learning material-en_US.srt
1. What is the Appendix-en_US.srt
2. BONUS Where to get discount coupons and FREE deep learning material.mp4
1. What is the Appendix.mp4
7. Temporal Difference Learning
5. SARSA in Code-en_US.srt
2. TD(0) Prediction-en_US.srt
6. Q Learning-en_US.srt
7. Q Learning in Code-en_US.srt
3. TD(0) Prediction in Code-en_US.srt
4. SARSA-en_US.srt
1. Temporal Difference Introduction-en_US.srt
8. TD Learning Section Summary-en_US.srt
5. SARSA in Code.mp4
7. Q Learning in Code.mp4
3. TD(0) Prediction in Code.mp4
6. Q Learning.mp4
4. SARSA.mp4
2. TD(0) Prediction.mp4
1. Temporal Difference Introduction.mp4
8. TD Learning Section Summary.mp4
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