Udemy - Artificial Intelligence: Reinforcement Learning in Python...

Files:

• 1. Introduction.mp4 (34.2 MB)
• 1. Introduction.srt (4.2 KB)
• 2. Where to get the Code.mp4 (4.4 MB)
• 2. Where to get the Code.srt (5.4 KB)
• 3. Strategy for Passing the Course.mp4 (9.5 MB)
• 3. Strategy for Passing the Course.srt (11.8 KB)
• 4. Course Outline.mp4 (31.0 MB)
• 4. Course Outline.srt (6.8 KB)

• 1. Stock Trading Project Section Introduction.mp4 (26.8 MB)
• 1. Stock Trading Project Section Introduction.srt (6.8 KB)
• 2. Data and Environment.mp4 (52.0 MB)
• 2. Data and Environment.srt (15.7 KB)
• 3. How to Model Q for Q-Learning.mp4 (44.9 MB)
• 3. How to Model Q for Q-Learning.srt (12.0 KB)
• 4. Design of the Program.mp4 (23.3 MB)
• 4. Design of the Program.srt (8.5 KB)
• 5. Code pt 1.mp4 (49.7 MB)
• 5. Code pt 1.srt (9.6 KB)
• 6. Code pt 2.mp4 (65.3 MB)
• 6. Code pt 2.srt (11.8 KB)
• 7. Code pt 3.mp4 (33.7 MB)
• 7. Code pt 3.srt (5.4 KB)
• 8. Code pt 4.mp4 (49.1 MB)
• 8. Code pt 4.srt (8.0 KB)
• 9. Stock Trading Project Discussion.mp4 (15.8 MB)
• 9. Stock Trading Project Discussion.srt (4.3 KB)

• 1. What is the Appendix.mp4 (5.5 MB)
• 1. What is the Appendix.srt (3.7 KB)
• 10. What order should I take your courses in (part 1).mp4 (29.3 MB)
• 10. What order should I take your courses in (part 1).srt (16.0 KB)
• 11. What order should I take your courses in (part 2).mp4 (37.6 MB)
• 11. What order should I take your courses in (part 2).srt (23.0 KB)
• 12. BONUS Where to get discount coupons and FREE deep learning material.mp4 (37.8 MB)
• 12. BONUS Where to get discount coupons and FREE deep learning material.srt (7.9 KB)
• 2. Windows-Focused Environment Setup 2018.mp4 (186.4 MB)
• 2. Windows-Focused Environment Setup 2018.srt (20.1 KB)
• 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (43.9 MB)
• 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt (18.3 KB)
• 4. How to Code by Yourself (part 1).mp4 (24.5 MB)
• 4. How to Code by Yourself (part 1).srt (30.2 KB)
• 5. How to Code by Yourself (part 2).mp4 (14.8 MB)
• 5. How to Code by Yourself (part 2).srt (18.4 KB)
• 6. How to Succeed in this Course (Long Version).mp4 (18.3 MB)
• 6. How to Succeed in this Course (Long Version).srt (14.5 KB)
• 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (39.0 MB)
• 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt (31.8 KB)
• 8. Proof that using Jupyter Notebook is the same as not using it.mp4 (78.3 MB)
• 8. Proof that using Jupyter Notebook is the same as not using it.srt (14.1 KB)
• 9. Python 2 vs Python 3.mp4 (7.8 MB)
• 9. Python 2 vs Python 3.srt (6.1 KB)

• 1. Problem Setup and The Explore-Exploit Dilemma.mp4 (6.5 MB)
• 1. Problem Setup and The Explore-Exploit Dilemma.srt (7.8 KB)
• 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 (10.6 MB)
• 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt (6.1 KB)
• 11. Nonstationary Bandits.mp4 (7.5 MB)
• 11. Nonstationary Bandits.srt (7.8 KB)
• 12. Bandit Summary, Real Data, and Online Learning.mp4 (33.9 MB)
• 12. Bandit Summary, Real Data, and Online Learning.srt (9.1 KB)
• 2. Applications of the Explore-Exploit Dilemma.mp4 (51.2 MB)
• 2. Applications of the Explore-Exploit Dilemma.srt (10.9 KB)
• 3. Epsilon-Greedy.mp4 (2.8 MB)
• 3. Epsilon-Greedy.srt (3.2 KB)
• 4. Updating a Sample Mean.mp4 (2.2 MB)
• 4. Updating a Sample Mean.srt (2.2 KB)
• 5. Designing Your Bandit Program.mp4 (24.5 MB)
• 5. Designing Your Bandit Program.srt (5.6 KB)
• 6. Comparing Different Epsilons.mp4 (8.0 MB)
• 6. Comparing Different Epsilons.srt (5.3 KB)
• 7. Optimistic Initial Values.mp4 (15.8 MB)
• 7. Optimistic Initial Values.srt (3.1 KB)
• 8. UCB1.mp4 (8.2 MB)
• 8. UCB1.srt (8.1 KB)
• 9. Bayesian Thompson Sampling.mp4 (51.8 MB)
• 9. Bayesian Thompson Sampling.srt (11.8 KB)

• 1. What is Reinforcement Learning.mp4 (54.6 MB)
• 1. What is Reinforcement Learning.srt (10.9 KB)
• 2. On Unusual or Unexpected Strategies of RL.mp4 (37.1 MB)
• 2. On Unusual or Unexpected Strategies of RL.srt (7.9 KB)
• 3. Defining Some Terms.mp4 (42.3 MB)
• 3. Defining Some Terms.srt (9.1 KB)

• 1. Naive Solution to Tic-Tac-Toe.mp4 (6.1 MB)
• 1. Naive Solution to Tic-Tac-Toe.srt (7.2 KB)
• 10. Tic Tac Toe Code Main Loop and Demo.mp4 (9.4 MB)
• 10. Tic Tac Toe Code Main Loop and Demo.srt (9.2 KB)
• 11. Tic Tac Toe Summary.mp4 (8.3 MB)
• 11. Tic Tac Toe Summary.srt (10.2 KB)
• 12. Tic Tac Toe Exercise.mp4 (19.8 MB)
• 12. Tic Tac Toe Exercise.srt (4.6 KB)
• 2. Components of a Reinforcement Learning System.mp4 (12.7 MB)
• 2. Components of a Reinforcement Learning System.srt (14.8 KB)
• 3. Notes on Assigning Rewards.mp4 (4.2 MB)
• 3. Notes on Assigning Rewards.srt (4.9 KB)
• 4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 (103.7 MB)
• 4. The Value Function and Your First Reinforcement Learning Algorithm.srt (22.8 KB)
• 5. Tic Tac Toe Code Outline.mp4 (5.0 MB)
• 5. Tic Tac Toe Code Outline.srt (6.4 KB)
• 6. Tic Tac Toe Code Representing States.mp4 (4.4 MB)
• 6. Tic Tac Toe Code Representing States.srt (4.9 KB)
• 7. Tic Tac Toe Code Enumerating States Recursively.mp4 (9.8 MB)
• 7. Tic Tac Toe Code Enumerating States Recursively.srt (11.3 KB)
• 8. Tic Tac Toe Code The Environment.mp4 (10.0 MB)
• 8. Tic Tac Toe Code The Environment.srt (12.0 KB)
• 9. Tic Tac Toe Code The Agent.mp4 (9.0 MB)
• 9. Tic Tac Toe Code The Agent.srt (10.9 KB)

• 1. Gridworld.mp4 (3.4 MB)
• 1. Gridworld.srt (4.0 KB)
• 2. The Markov Property.mp4 (7.2 MB)
• 2. The Markov Property.srt (8.4 KB)
• 3. Defining and Formalizing the MDP.mp4 (6.6 MB)
• 3. Defining and Formalizing the MDP.srt (7.9 KB)
• 4. Future Rewards.mp4 (5.2 MB)
• 4. Future Rewards.srt (6.0 KB)
• 5. Value Function Introduction.mp4 (19.7 MB)
• 5. Value Function Introduction.srt (15.6 KB)
• 6. Value Functions.mp4 (8.3 MB)
• 6. Value Functions.srt (11.8 KB)
• 7. Bellman Examples.mp4 (87.1 MB)
• 7. Bellman Examples.srt (27.7 KB)
• 8. Optimal Policy and Optimal Value Function.mp4 (3.2 MB)
• 8. Optimal Policy and Optimal Value Function.srt (5.0 KB)
• 9. MDP Summary.mp4 (5.7 MB)
• 9. MDP Summary.srt (2.0 KB)

• 1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 (4.8 MB)
• 1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt (5.4 KB)
• 10. Value Iteration in Code.mp4 (4.9 MB)
• 10. Value Iteration in Code.srt (3.3 KB)
• 11. Dynamic Programming Summary.mp4 (8.3 MB)
• 11. Dynamic Programming Summary.srt (9.4 KB)
• 2. Gridworld in Code.mp4 (11.5 MB)
• 2. Gridworld in Code.srt (11.0 KB)
• 3. Designing Your RL Program.mp4 (22.3 MB)
• 3. Designing Your RL Program.srt (6.6 KB)
• 4. Iterative Policy Evaluation in Code.mp4 (12.1 MB)
• 4. Iterative Policy Evaluation in Code.srt (10.2 KB)
• 5. Policy Improvement.mp4 (4.5 MB)
• 5. Policy Improvement.srt (5.2 KB)
• 6. Policy Iteration.mp4 (3.1 MB)
• 6. Policy Iteration.srt (3.5 KB)
• 7. Policy Iteration in Code.mp4 (7.6 MB)
• 7. Policy Iteration in Code.srt (6.1 KB)
• 8. Policy Iteration in Windy Gridworld.mp4 (9.1 MB)
• 8. Policy Iteration in Windy Gridworld.srt (8.2 KB)
• 9. Value Iteration.mp4 (6.2 MB)
• 9. Value Iteration.srt (7.0 KB)

• 1. Monte Carlo Intro.mp4 (5.0 MB)
• 1. Monte Carlo Intro.srt (6.0 KB)
• 2. Monte Carlo Policy Evaluation.mp4 (8.8 MB)
• 2. Monte Carlo Policy Evaluation.srt (10.8 KB)
• 3. Monte Carlo Policy Evaluation in Code.mp4 (7.9 MB)
• 3. Monte Carlo Policy Evaluation in Code.srt (6.1 KB)
• 4. Policy Evaluation in Windy Gridworld.mp4 (7.8 MB)
• 4. Policy Evaluation in Windy Gridworld.srt (5.3 KB)
• 5. Monte Carlo Control.mp4 (9.3 MB)
• 5. Monte Carlo Control.srt (10.2 KB)
• 6. Monte Carlo Control in Code.mp4 (10.2 MB)
• 6. Monte Carlo Control in Code.srt (5.8 KB)
• 7. Monte Carlo Control without Exploring Starts.mp4 (4.6 MB)
• 7. Monte Carlo Control without Exploring Starts.srt (5.5 KB)
• 8. Monte Carlo Control without Exploring Starts in Code.mp4 (8.1 MB)
• 8. Monte Carlo Control without Exploring Starts in Code.srt (3.6 KB)
• 9. Monte Carlo Summary.mp4 (5.7 MB)
• 9. Monte Carlo Summary.srt (7.1 KB)

• 1. Temporal Difference Intro.mp4 (2.7 MB)
• 1. Temporal Difference Intro.srt (3.3 KB)
• 2. TD(0) Prediction.mp4 (5.8 MB)
• 2. TD(0) Prediction.srt (6.4 KB)
• 3. TD(0) Prediction in Code.mp4 (5.3 MB)
• 3. TD(0) Prediction in Code.srt (4.0 KB)
• 4. SARSA.mp4 (8.2 MB)
• 4. SARSA.srt (9.7 KB)
• 5. SARSA in Code.mp4 (8.8 MB)
• 5. SARSA in Code.srt (5.5 KB)
• 6. Q Learning.mp4 (4.8 MB)
• 6. Q Learning.srt (5.8 KB)
• 7. Q Learning in Code.mp4 (5.4 MB)
• 7. Q Learning in Code.srt (3.5 KB)
• 8. TD Summary.mp4 (3.9 MB)
• 8. TD Summary.srt (4.7 KB)

• 1. Approximation Intro.mp4 (6.5 MB)
• 1. Approximation Intro.srt (8.0 KB)
• 2. Linear Models for Reinforcement Learning.mp4 (6.5 MB)
• 2. Linear Models for Reinforcement Learning.srt (7.4 KB)
• 3. Features.mp4 (6.3 MB)
• 3. Features.srt (6.9 KB)
• 4. Monte Carlo Prediction with Approximation.mp4 (2.8 MB)
• 4. Monte Carlo Prediction with Approximation.srt (2.3 KB)
• 5. Monte Carlo Prediction with Approximation in Code.mp4 (6.6 MB)
• 5. Monte Carlo Prediction with Approximation in Code.srt (4.0 KB)
• 6. TD(0) Semi-Gradient Prediction.mp4 (8.4 MB)
• 6. TD(0) Semi-Gradient Prediction.srt (6.4 KB)
• 7. Semi-Gradient SARSA.mp4 (4.7 MB)
• 7. Semi-Gradient SARSA.srt (5.5 KB)
• 8. Semi-Gradient SARSA in Code.mp4 (10.6 MB)
• 8. Semi-Gradient SARSA in Code.srt (5.4 KB)
• 9. Course Summary and Next Steps.mp4 (13.2 MB)
• 9. Course Summary and Next Steps.srt (16.0 KB)

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