Udemy - Artificial Intelligence: Reinforcement Learning in Python [GC]

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Udemy - Artificial Intelligence: Reinforcement Learning in Python [GC]

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Udemy - Artificial Intelligence: Reinforcement Learning in Python [GC]
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1. Approximation Intro.mp4
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6.5 MB
1. Approximation Intro.srt
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1. Gridworld.mp4
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1. Gridworld.srt
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1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4
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1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt
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1. Introduction.mp4
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1. Introduction.srt
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1. Monte Carlo Intro.mp4
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1. Monte Carlo Intro.srt
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1. Naive Solution to Tic-Tac-Toe.mp4
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1. Naive Solution to Tic-Tac-Toe.srt
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1. Problem Setup and The Explore-Exploit Dilemma.mp4
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1. Problem Setup and The Explore-Exploit Dilemma.srt
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1. Stock Trading Project Section Introduction.mp4
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1. Stock Trading Project Section Introduction.srt
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1. Temporal Difference Intro.mp4
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1. Temporal Difference Intro.srt
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1. What is Reinforcement Learning.mp4
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1. What is Reinforcement Learning.srt
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1. What is the Appendix.mp4
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1. What is the Appendix.srt
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10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4
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10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt
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10. Tic Tac Toe Code Main Loop and Demo.mp4
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10. Tic Tac Toe Code Main Loop and Demo.srt
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10. Value Iteration in Code.mp4
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10. Value Iteration in Code.srt
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10. What order should I take your courses in (part 1).mp4
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10. What order should I take your courses in (part 1).srt
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11. Dynamic Programming Summary.mp4
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11. Dynamic Programming Summary.srt
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11. Nonstationary Bandits.mp4
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11. Nonstationary Bandits.srt
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11. Tic Tac Toe Summary.mp4
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11. Tic Tac Toe Summary.srt
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11. What order should I take your courses in (part 2).mp4
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11. What order should I take your courses in (part 2).srt
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12. BONUS Where to get discount coupons and FREE deep learning material.mp4
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12. BONUS Where to get discount coupons and FREE deep learning material.srt
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12. Bandit Summary, Real Data, and Online Learning.mp4
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12. Bandit Summary, Real Data, and Online Learning.srt
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12. Tic Tac Toe Exercise.mp4
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12. Tic Tac Toe Exercise.srt
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2. Applications of the Explore-Exploit Dilemma.mp4
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2. Applications of the Explore-Exploit Dilemma.srt
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2. Components of a Reinforcement Learning System.mp4
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2. Components of a Reinforcement Learning System.srt
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2. Data and Environment.mp4
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2. Data and Environment.srt
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2. Gridworld in Code.mp4
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2. Gridworld in Code.srt
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2. Linear Models for Reinforcement Learning.mp4
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2. Linear Models for Reinforcement Learning.srt
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2. Monte Carlo Policy Evaluation.mp4
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2. Monte Carlo Policy Evaluation.srt
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2. On Unusual or Unexpected Strategies of RL.mp4
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2. On Unusual or Unexpected Strategies of RL.srt
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2. TD(0) Prediction.mp4
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2. TD(0) Prediction.srt
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2. The Markov Property.mp4
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2. The Markov Property.srt
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2. Where to get the Code.mp4
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2. Where to get the Code.srt
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2. Windows-Focused Environment Setup 2018.mp4
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2. Windows-Focused Environment Setup 2018.srt
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3. Defining Some Terms.mp4
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3. Defining Some Terms.srt
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3. Defining and Formalizing the MDP.mp4
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3. Defining and Formalizing the MDP.srt
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3. Designing Your RL Program.mp4
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3. Designing Your RL Program.srt
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3. Epsilon-Greedy.mp4
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3. Epsilon-Greedy.srt
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3. Features.mp4
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3. Features.srt
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3. How to Model Q for Q-Learning.mp4
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3. How to Model Q for Q-Learning.srt
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3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4
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3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt
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3. Monte Carlo Policy Evaluation in Code.mp4
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3. Monte Carlo Policy Evaluation in Code.srt
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3. Notes on Assigning Rewards.mp4
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3. Notes on Assigning Rewards.srt
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3. Strategy for Passing the Course.mp4
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3. Strategy for Passing the Course.srt
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3. TD(0) Prediction in Code.mp4
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3. TD(0) Prediction in Code.srt
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4. Course Outline.mp4
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4. Course Outline.srt
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4. Design of the Program.mp4
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4. Design of the Program.srt
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4. Future Rewards.mp4
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4. Future Rewards.srt
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4. How to Code by Yourself (part 1).mp4
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4. How to Code by Yourself (part 1).srt
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4. Iterative Policy Evaluation in Code.mp4
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4. Iterative Policy Evaluation in Code.srt
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4. Monte Carlo Prediction with Approximation.mp4
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4. Monte Carlo Prediction with Approximation.srt
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4. Policy Evaluation in Windy Gridworld.mp4
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4. Policy Evaluation in Windy Gridworld.srt
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4. SARSA.mp4
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4. SARSA.srt
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4. The Value Function and Your First Reinforcement Learning Algorithm.mp4
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4. The Value Function and Your First Reinforcement Learning Algorithm.srt
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4. Updating a Sample Mean.mp4
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4. Updating a Sample Mean.srt
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5. Code pt 1.mp4
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5. Code pt 1.srt
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5. Designing Your Bandit Program.mp4
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5. Designing Your Bandit Program.srt
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5. How to Code by Yourself (part 2).mp4
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5. How to Code by Yourself (part 2).srt
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5. Monte Carlo Control.mp4
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5. Monte Carlo Control.srt
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5. Monte Carlo Prediction with Approximation in Code.mp4
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5. Monte Carlo Prediction with Approximation in Code.srt
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5. Policy Improvement.mp4
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5. Policy Improvement.srt
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5. SARSA in Code.mp4
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5. SARSA in Code.srt
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5. Tic Tac Toe Code Outline.mp4
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5. Tic Tac Toe Code Outline.srt
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5. Value Function Introduction.mp4
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5. Value Function Introduction.srt
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6. Code pt 2.mp4
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6. Code pt 2.srt
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6. Comparing Different Epsilons.mp4
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6. Comparing Different Epsilons.srt
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6. How to Succeed in this Course (Long Version).mp4
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6. How to Succeed in this Course (Long Version).srt
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6. Monte Carlo Control in Code.mp4
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6. Monte Carlo Control in Code.srt
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6. Policy Iteration.mp4
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6. Policy Iteration.srt
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6. Q Learning.mp4
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6. Q Learning.srt
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6. TD(0) Semi-Gradient Prediction.mp4
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6. TD(0) Semi-Gradient Prediction.srt
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6. Tic Tac Toe Code Representing States.mp4
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6. Tic Tac Toe Code Representing States.srt
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6. Value Functions.mp4
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6. Value Functions.srt
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7. Bellman Examples.mp4
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7. Bellman Examples.srt
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7. Code pt 3.mp4
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7. Code pt 3.srt
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7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4
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7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt
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7. Monte Carlo Control without Exploring Starts.mp4
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7. Monte Carlo Control without Exploring Starts.srt
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7. Optimistic Initial Values.mp4
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7. Optimistic Initial Values.srt
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7. Policy Iteration in Code.mp4
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7. Policy Iteration in Code.srt
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7. Q Learning in Code.mp4
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7. Semi-Gradient SARSA.mp4
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7. Semi-Gradient SARSA.srt
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7. Tic Tac Toe Code Enumerating States Recursively.mp4
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8. Code pt 4.mp4
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8. Code pt 4.srt
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8. Monte Carlo Control without Exploring Starts in Code.mp4
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8. Optimal Policy and Optimal Value Function.mp4
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8. Policy Iteration in Windy Gridworld.mp4
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8. Proof that using Jupyter Notebook is the same as not using it.mp4
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8. Proof that using Jupyter Notebook is the same as not using it.srt
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8. Semi-Gradient SARSA in Code.mp4
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8. Semi-Gradient SARSA in Code.srt
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8. TD Summary.mp4
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8. TD Summary.srt
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8. Tic Tac Toe Code The Environment.mp4
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8. Tic Tac Toe Code The Environment.srt
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8. UCB1.mp4
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8. UCB1.srt
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9. Bayesian Thompson Sampling.mp4
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9. Bayesian Thompson Sampling.srt
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9. Course Summary and Next Steps.mp4
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9. Course Summary and Next Steps.srt
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9. MDP Summary.mp4
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9. MDP Summary.srt
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9. Monte Carlo Summary.mp4
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9. Monte Carlo Summary.srt
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9. Python 2 vs Python 3.mp4
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9. Python 2 vs Python 3.srt
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9. Stock Trading Project Discussion.mp4
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9. Tic Tac Toe Code The Agent.mp4
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9. Tic Tac Toe Code The Agent.srt
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9. Value Iteration.mp4
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9. Value Iteration.srt
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