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

10. Appendix/2. Windows-Focused Environment Setup 2018.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4

5. Markov Decision Proccesses/7. Bellman Examples.mp4

10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4

2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.mp4

3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp4

3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4

10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4

2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.vtt

2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.mp4

10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4

10. Appendix/11. What order should I take your courses in (part 2).mp4

2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.mp4

1. Welcome/1. Introduction.mp4

2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.mp4

10. Appendix/10. What order should I take your courses in (part 1).mp4

10. Appendix/4. How to Code by Yourself (part 1).mp4

3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp4

6. Dynamic Programming/3. Designing Your RL Program.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp4

5. Markov Decision Proccesses/5. Value Function Introduction.mp4

10. Appendix/6. How to Succeed in this Course (Long Version).mp4

10. Appendix/5. How to Code by Yourself (part 2).mp4

9. Approximation Methods/9. Course Summary and Next Steps.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4

6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp4

6. Dynamic Programming/2. Gridworld in Code.mp4

9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4

3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4

7. Monte Carlo/6. Monte Carlo Control in Code.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4

1. Welcome/3. Strategy for Passing the Course.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4

7. Monte Carlo/5. Monte Carlo Control.mp4

6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp4

8. Temporal Difference Learning/5. SARSA in Code.mp4

7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4

9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4

6. Dynamic Programming/11. Dynamic Programming Summary.mp4

4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4

5. Markov Decision Proccesses/6. Value Functions.mp4

3. Return of the Multi-Armed Bandit/8. UCB1.mp4

8. Temporal Difference Learning/4. SARSA.mp4

7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4

3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp4

7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4

10. Appendix/9. Python 2 vs Python 3.mp4

7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4