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| [CourseClub.Me].url |
48б |
| [DesireCourse.Net].url |
51б |
| [FreeCourseWorld.Com].url |
54б |
| 1. Approximation Intro.mp4 |
6.47Мб |
| 1. Approximation Intro.srt |
7.99Кб |
| 1. Gridworld.mp4 |
3.36Мб |
| 1. Gridworld.srt |
4.04Кб |
| 1. Introduction.mp4 |
34.24Мб |
| 1. Introduction.srt |
4.17Кб |
| 1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 |
4.83Мб |
| 1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt |
5.37Кб |
| 1. Monte Carlo Intro.mp4 |
4.97Мб |
| 1. Monte Carlo Intro.srt |
5.96Кб |
| 1. Naive Solution to Tic-Tac-Toe.mp4 |
6.12Мб |
| 1. Naive Solution to Tic-Tac-Toe.srt |
7.21Кб |
| 1. Problem Setup and The Explore-Exploit Dilemma.mp4 |
6.47Мб |
| 1. Problem Setup and The Explore-Exploit Dilemma.srt |
7.80Кб |
| 1. Stock Trading Project Section Introduction.mp4 |
26.77Мб |
| 1. Stock Trading Project Section Introduction.srt |
6.84Кб |
| 1. Temporal Difference Intro.mp4 |
2.73Мб |
| 1. Temporal Difference Intro.srt |
3.33Кб |
| 1. What is Reinforcement Learning.mp4 |
54.62Мб |
| 1. What is Reinforcement Learning.srt |
10.92Кб |
| 1. What is the Appendix.mp4 |
5.45Мб |
| 1. What is the Appendix.srt |
3.72Кб |
| 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 |
10.57Мб |
| 10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt |
6.09Кб |
| 10. Tic Tac Toe Code Main Loop and Demo.mp4 |
9.44Мб |
| 10. Tic Tac Toe Code Main Loop and Demo.srt |
9.25Кб |
| 10. Value Iteration in Code.mp4 |
4.89Мб |
| 10. Value Iteration in Code.srt |
3.34Кб |
| 10. What order should I take your courses in (part 1).mp4 |
29.32Мб |
| 10. What order should I take your courses in (part 1).srt |
16.03Кб |
| 11. Dynamic Programming Summary.mp4 |
8.31Мб |
| 11. Dynamic Programming Summary.srt |
9.39Кб |
| 11. Nonstationary Bandits.mp4 |
7.48Мб |
| 11. Nonstationary Bandits.srt |
7.79Кб |
| 11. Tic Tac Toe Summary.mp4 |
8.32Мб |
| 11. Tic Tac Toe Summary.srt |
10.23Кб |
| 11. What order should I take your courses in (part 2).mp4 |
37.62Мб |
| 11. What order should I take your courses in (part 2).srt |
23.04Кб |
| 12. Bandit Summary, Real Data, and Online Learning.mp4 |
33.92Мб |
| 12. Bandit Summary, Real Data, and Online Learning.srt |
9.12Кб |
| 12. BONUS Where to get discount coupons and FREE deep learning material.mp4 |
37.83Мб |
| 12. BONUS Where to get discount coupons and FREE deep learning material.srt |
7.87Кб |
| 12. Tic Tac Toe Exercise.mp4 |
19.77Мб |
| 12. Tic Tac Toe Exercise.srt |
4.63Кб |
| 2. Applications of the Explore-Exploit Dilemma.mp4 |
51.18Мб |
| 2. Applications of the Explore-Exploit Dilemma.srt |
10.92Кб |
| 2. Components of a Reinforcement Learning System.mp4 |
12.71Мб |
| 2. Components of a Reinforcement Learning System.srt |
14.79Кб |
| 2. Data and Environment.mp4 |
52.01Мб |
| 2. Data and Environment.srt |
15.69Кб |
| 2. Gridworld in Code.mp4 |
11.46Мб |
| 2. Gridworld in Code.srt |
10.98Кб |
| 2. Linear Models for Reinforcement Learning.mp4 |
6.47Мб |
| 2. Linear Models for Reinforcement Learning.srt |
7.39Кб |
| 2. Monte Carlo Policy Evaluation.mp4 |
8.75Мб |
| 2. Monte Carlo Policy Evaluation.srt |
10.84Кб |
| 2. On Unusual or Unexpected Strategies of RL.mp4 |
37.10Мб |
| 2. On Unusual or Unexpected Strategies of RL.srt |
7.95Кб |
| 2. TD(0) Prediction.mp4 |
5.82Мб |
| 2. TD(0) Prediction.srt |
6.38Кб |
| 2. The Markov Property.mp4 |
7.18Мб |
| 2. The Markov Property.srt |
8.43Кб |
| 2. Where to get the Code.mp4 |
4.45Мб |
| 2. Where to get the Code.srt |
5.41Кб |
| 2. Windows-Focused Environment Setup 2018.mp4 |
186.38Мб |
| 2. Windows-Focused Environment Setup 2018.srt |
20.10Кб |
| 3. Defining and Formalizing the MDP.mp4 |
6.64Мб |
| 3. Defining and Formalizing the MDP.srt |
7.87Кб |
| 3. Defining Some Terms.mp4 |
42.34Мб |
| 3. Defining Some Terms.srt |
9.15Кб |
| 3. Designing Your RL Program.mp4 |
22.34Мб |
| 3. Designing Your RL Program.srt |
6.64Кб |
| 3. Epsilon-Greedy.mp4 |
2.78Мб |
| 3. Epsilon-Greedy.srt |
3.21Кб |
| 3. Features.mp4 |
6.25Мб |
| 3. Features.srt |
6.94Кб |
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
43.92Мб |
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt |
18.33Кб |
| 3. How to Model Q for Q-Learning.mp4 |
44.89Мб |
| 3. How to Model Q for Q-Learning.srt |
12.01Кб |
| 3. Monte Carlo Policy Evaluation in Code.mp4 |
7.92Мб |
| 3. Monte Carlo Policy Evaluation in Code.srt |
6.12Кб |
| 3. Notes on Assigning Rewards.mp4 |
4.22Мб |
| 3. Notes on Assigning Rewards.srt |
4.94Кб |
| 3. Strategy for Passing the Course.mp4 |
9.48Мб |
| 3. Strategy for Passing the Course.srt |
11.77Кб |
| 3. TD(0) Prediction in Code.mp4 |
5.32Мб |
| 3. TD(0) Prediction in Code.srt |
3.97Кб |
| 4. Course Outline.mp4 |
30.97Мб |
| 4. Course Outline.srt |
6.81Кб |
| 4. Design of the Program.mp4 |
23.31Мб |
| 4. Design of the Program.srt |
8.51Кб |
| 4. Future Rewards.mp4 |
5.17Мб |
| 4. Future Rewards.srt |
6.01Кб |
| 4. How to Code by Yourself (part 1).mp4 |
24.54Мб |
| 4. How to Code by Yourself (part 1).srt |
30.21Кб |
| 4. Iterative Policy Evaluation in Code.mp4 |
12.06Мб |
| 4. Iterative Policy Evaluation in Code.srt |
10.24Кб |
| 4. Monte Carlo Prediction with Approximation.mp4 |
2.85Мб |
| 4. Monte Carlo Prediction with Approximation.srt |
2.34Кб |
| 4. Policy Evaluation in Windy Gridworld.mp4 |
7.81Мб |
| 4. Policy Evaluation in Windy Gridworld.srt |
5.30Кб |
| 4. SARSA.mp4 |
8.20Мб |
| 4. SARSA.srt |
9.70Кб |
| 4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 |
103.72Мб |
| 4. The Value Function and Your First Reinforcement Learning Algorithm.srt |
22.78Кб |
| 4. Updating a Sample Mean.mp4 |
2.18Мб |
| 4. Updating a Sample Mean.srt |
2.17Кб |
| 5. Code pt 1.mp4 |
49.72Мб |
| 5. Code pt 1.srt |
9.63Кб |
| 5. Designing Your Bandit Program.mp4 |
24.51Мб |
| 5. Designing Your Bandit Program.srt |
5.61Кб |
| 5. How to Code by Yourself (part 2).mp4 |
14.80Мб |
| 5. How to Code by Yourself (part 2).srt |
18.42Кб |
| 5. Monte Carlo Control.mp4 |
9.26Мб |
| 5. Monte Carlo Control.srt |
10.24Кб |
| 5. Monte Carlo Prediction with Approximation in Code.mp4 |
6.57Мб |
| 5. Monte Carlo Prediction with Approximation in Code.srt |
4.01Кб |
| 5. Policy Improvement.mp4 |
4.53Мб |
| 5. Policy Improvement.srt |
5.17Кб |
| 5. SARSA in Code.mp4 |
8.82Мб |
| 5. SARSA in Code.srt |
5.53Кб |
| 5. Tic Tac Toe Code Outline.mp4 |
5.04Мб |
| 5. Tic Tac Toe Code Outline.srt |
6.42Кб |
| 5. Value Function Introduction.mp4 |
19.72Мб |
| 5. Value Function Introduction.srt |
15.63Кб |
| 6. Code pt 2.mp4 |
65.29Мб |
| 6. Code pt 2.srt |
11.75Кб |
| 6. Comparing Different Epsilons.mp4 |
8.01Мб |
| 6. Comparing Different Epsilons.srt |
5.32Кб |
| 6. How to Succeed in this Course (Long Version).mp4 |
18.31Мб |
| 6. How to Succeed in this Course (Long Version).srt |
14.55Кб |
| 6. Monte Carlo Control in Code.mp4 |
10.17Мб |
| 6. Monte Carlo Control in Code.srt |
5.83Кб |
| 6. Policy Iteration.mp4 |
3.14Мб |
| 6. Policy Iteration.srt |
3.47Кб |
| 6. Q Learning.mp4 |
4.84Мб |
| 6. Q Learning.srt |
5.82Кб |
| 6. TD(0) Semi-Gradient Prediction.mp4 |
8.36Мб |
| 6. TD(0) Semi-Gradient Prediction.srt |
6.36Кб |
| 6. Tic Tac Toe Code Representing States.mp4 |
4.42Мб |
| 6. Tic Tac Toe Code Representing States.srt |
4.92Кб |
| 6. Value Functions.mp4 |
8.29Мб |
| 6. Value Functions.srt |
11.76Кб |
| 7. Bellman Examples.mp4 |
87.12Мб |
| 7. Bellman Examples.srt |
27.67Кб |
| 7. Code pt 3.mp4 |
33.72Мб |
| 7. Code pt 3.srt |
5.41Кб |
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
38.95Мб |
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt |
31.79Кб |
| 7. Monte Carlo Control without Exploring Starts.mp4 |
4.62Мб |
| 7. Monte Carlo Control without Exploring Starts.srt |
5.53Кб |
| 7. Optimistic Initial Values.mp4 |
15.84Мб |
| 7. Optimistic Initial Values.srt |
3.06Кб |
| 7. Policy Iteration in Code.mp4 |
7.62Мб |
| 7. Policy Iteration in Code.srt |
6.08Кб |
| 7. Q Learning in Code.mp4 |
5.42Мб |
| 7. Q Learning in Code.srt |
3.46Кб |
| 7. Semi-Gradient SARSA.mp4 |
4.70Мб |
| 7. Semi-Gradient SARSA.srt |
5.47Кб |
| 7. Tic Tac Toe Code Enumerating States Recursively.mp4 |
9.79Мб |
| 7. Tic Tac Toe Code Enumerating States Recursively.srt |
11.33Кб |
| 8. Code pt 4.mp4 |
49.08Мб |
| 8. Code pt 4.srt |
8.04Кб |
| 8. Monte Carlo Control without Exploring Starts in Code.mp4 |
8.06Мб |
| 8. Monte Carlo Control without Exploring Starts in Code.srt |
3.63Кб |
| 8. Optimal Policy and Optimal Value Function.mp4 |
3.23Мб |
| 8. Optimal Policy and Optimal Value Function.srt |
4.96Кб |
| 8. Policy Iteration in Windy Gridworld.mp4 |
9.10Мб |
| 8. Policy Iteration in Windy Gridworld.srt |
8.23Кб |
| 8. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.32Мб |
| 8. Proof that using Jupyter Notebook is the same as not using it.srt |
14.12Кб |
| 8. Semi-Gradient SARSA in Code.mp4 |
10.61Мб |
| 8. Semi-Gradient SARSA in Code.srt |
5.40Кб |
| 8. TD Summary.mp4 |
3.94Мб |
| 8. TD Summary.srt |
4.66Кб |
| 8. Tic Tac Toe Code The Environment.mp4 |
10.05Мб |
| 8. Tic Tac Toe Code The Environment.srt |
11.97Кб |
| 8. UCB1.mp4 |
8.23Мб |
| 8. UCB1.srt |
8.13Кб |
| 9. Bayesian Thompson Sampling.mp4 |
51.85Мб |
| 9. Bayesian Thompson Sampling.srt |
11.80Кб |
| 9. Course Summary and Next Steps.mp4 |
13.24Мб |
| 9. Course Summary and Next Steps.srt |
15.95Кб |
| 9. MDP Summary.mp4 |
5.67Мб |
| 9. MDP Summary.srt |
1.99Кб |
| 9. Monte Carlo Summary.mp4 |
5.71Мб |
| 9. Monte Carlo Summary.srt |
7.10Кб |
| 9. Python 2 vs Python 3.mp4 |
7.84Мб |
| 9. Python 2 vs Python 3.srt |
6.10Кб |
| 9. Stock Trading Project Discussion.mp4 |
15.78Мб |
| 9. Stock Trading Project Discussion.srt |
4.35Кб |
| 9. Tic Tac Toe Code The Agent.mp4 |
9.01Мб |
| 9. Tic Tac Toe Code The Agent.srt |
10.95Кб |
| 9. Value Iteration.mp4 |
6.18Мб |
| 9. Value Iteration.srt |
6.97Кб |