| 1. Approximation Intro.mp4 | 6.5 MB | ||
| 1. Approximation Intro.srt | 8 KB | ||
| 1. Gridworld.mp4 | 3.4 MB | ||
| 1. Gridworld.srt | 4 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 | ||
| 1. Introduction.mp4 | 34.2 MB | ||
| 1. Introduction.srt | 4.2 KB | ||
| 1. Monte Carlo Intro.mp4 | 5 MB | ||
| 1. Monte Carlo Intro.srt | 6 KB | ||
| 1. Naive Solution to Tic-Tac-Toe.mp4 | 6.1 MB | ||
| 1. Naive Solution to Tic-Tac-Toe.srt | 7.2 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 | ||
| 1. Stock Trading Project Section Introduction.mp4 | 26.8 MB | ||
| 1. Stock Trading Project Section Introduction.srt | 6.8 KB | ||
| 1. Temporal Difference Intro.mp4 | 2.7 MB | ||
| 1. Temporal Difference Intro.srt | 3.3 KB | ||
| 1. What is Reinforcement Learning.mp4 | 54.6 MB | ||
| 1. What is Reinforcement Learning.srt | 10.9 KB | ||
| 1. What is the Appendix.mp4 | 5.5 MB | ||
| 1. What is the Appendix.srt | 3.7 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 | ||
| 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 | ||
| 10. Value Iteration in Code.mp4 | 4.9 MB | ||
| 10. Value Iteration in Code.srt | 3.3 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 KB | ||
| 11. Dynamic Programming Summary.mp4 | 8.3 MB | ||
| 11. Dynamic Programming Summary.srt | 9.4 KB | ||
| 11. Nonstationary Bandits.mp4 | 7.5 MB | ||
| 11. Nonstationary Bandits.srt | 7.8 KB | ||
| 11. Tic Tac Toe Summary.mp4 | 8.3 MB | ||
| 11. Tic Tac Toe Summary.srt | 10.2 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 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 | ||
| 12. Bandit Summary, Real Data, and Online Learning.mp4 | 33.9 MB | ||
| 12. Bandit Summary, Real Data, and Online Learning.srt | 9.1 KB | ||
| 12. Tic Tac Toe Exercise.mp4 | 19.8 MB | ||
| 12. Tic Tac Toe Exercise.srt | 4.6 KB | ||
| 2. Applications of the Explore-Exploit Dilemma.mp4 | 51.2 MB | ||
| 2. Applications of the Explore-Exploit Dilemma.srt | 10.9 KB | ||
| 2. Components of a Reinforcement Learning System.mp4 | 12.7 MB | ||
| 2. Components of a Reinforcement Learning System.srt | 14.8 KB | ||
| 2. Data and Environment.mp4 | 52 MB | ||
| 2. Data and Environment.srt | 15.7 KB | ||
| 2. Gridworld in Code.mp4 | 11.5 MB | ||
| 2. Gridworld in Code.srt | 11 KB | ||
| 2. Linear Models for Reinforcement Learning.mp4 | 6.5 MB | ||
| 2. Linear Models for Reinforcement Learning.srt | 7.4 KB | ||
| 2. Monte Carlo Policy Evaluation.mp4 | 8.8 MB | ||
| 2. Monte Carlo Policy Evaluation.srt | 10.8 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 | ||
| 2. TD(0) Prediction.mp4 | 5.8 MB | ||
| 2. TD(0) Prediction.srt | 6.4 KB | ||
| 2. The Markov Property.mp4 | 7.2 MB | ||
| 2. The Markov Property.srt | 8.4 KB | ||
| 2. Where to get the Code.mp4 | 4.4 MB | ||
| 2. Where to get the Code.srt | 5.4 KB | ||
| 2. Windows-Focused Environment Setup 2018.mp4 | 186.4 MB | ||
| 2. Windows-Focused Environment Setup 2018.srt | 20.1 KB | ||
| 3. Defining Some Terms.mp4 | 42.3 MB | ||
| 3. Defining Some Terms.srt | 9.1 KB | ||
| 3. Defining and Formalizing the MDP.mp4 | 6.6 MB | ||
| 3. Defining and Formalizing the MDP.srt | 7.9 KB | ||
| 3. Designing Your RL Program.mp4 | 22.3 MB | ||
| 3. Designing Your RL Program.srt | 6.6 KB | ||
| 3. Epsilon-Greedy.mp4 | 2.8 MB | ||
| 3. Epsilon-Greedy.srt | 3.2 KB | ||
| 3. Features.mp4 | 6.3 MB | ||
| 3. Features.srt | 6.9 KB | ||
| 3. How to Model Q for Q-Learning.mp4 | 44.9 MB | ||
| 3. How to Model Q for Q-Learning.srt | 12 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 | ||
| 3. Monte Carlo Policy Evaluation in Code.mp4 | 7.9 MB | ||
| 3. Monte Carlo Policy Evaluation in Code.srt | 6.1 KB | ||
| 3. Notes on Assigning Rewards.mp4 | 4.2 MB | ||
| 3. Notes on Assigning Rewards.srt | 4.9 KB | ||
| 3. Strategy for Passing the Course.mp4 | 9.5 MB | ||
| 3. Strategy for Passing the Course.srt | 11.8 KB | ||
| 3. TD(0) Prediction in Code.mp4 | 5.3 MB | ||
| 3. TD(0) Prediction in Code.srt | 4 KB | ||
| 4. Course Outline.mp4 | 31 MB | ||
| 4. Course Outline.srt | 6.8 KB | ||
| 4. Design of the Program.mp4 | 23.3 MB | ||
| 4. Design of the Program.srt | 8.5 KB | ||
| 4. Future Rewards.mp4 | 5.2 MB | ||
| 4. Future Rewards.srt | 6 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 | ||
| 4. Iterative Policy Evaluation in Code.mp4 | 12.1 MB | ||
| 4. Iterative Policy Evaluation in Code.srt | 10.2 KB | ||
| 4. Monte Carlo Prediction with Approximation.mp4 | 2.8 MB | ||
| 4. Monte Carlo Prediction with Approximation.srt | 2.3 KB | ||
| 4. Policy Evaluation in Windy Gridworld.mp4 | 7.8 MB | ||
| 4. Policy Evaluation in Windy Gridworld.srt | 5.3 KB | ||
| 4. SARSA.mp4 | 8.2 MB | ||
| 4. SARSA.srt | 9.7 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 | ||
| 4. Updating a Sample Mean.mp4 | 2.2 MB | ||
| 4. Updating a Sample Mean.srt | 2.2 KB | ||
| 5. Code pt 1.mp4 | 49.7 MB | ||
| 5. Code pt 1.srt | 9.6 KB | ||
| 5. Designing Your Bandit Program.mp4 | 24.5 MB | ||
| 5. Designing Your Bandit Program.srt | 5.6 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 | ||
| 5. Monte Carlo Control.mp4 | 9.3 MB | ||
| 5. Monte Carlo Control.srt | 10.2 KB | ||
| 5. Monte Carlo Prediction with Approximation in Code.mp4 | 6.6 MB | ||
| 5. Monte Carlo Prediction with Approximation in Code.srt | 4 KB | ||
| 5. Policy Improvement.mp4 | 4.5 MB | ||
| 5. Policy Improvement.srt | 5.2 KB | ||
| 5. SARSA in Code.mp4 | 8.8 MB | ||
| 5. SARSA in Code.srt | 5.5 KB | ||
| 5. Tic Tac Toe Code Outline.mp4 | 5 MB | ||
| 5. Tic Tac Toe Code Outline.srt | 6.4 KB | ||
| 5. Value Function Introduction.mp4 | 19.7 MB | ||
| 5. Value Function Introduction.srt | 15.6 KB | ||
| 6. Code pt 2.mp4 | 65.3 MB | ||
| 6. Code pt 2.srt | 11.8 KB | ||
| 6. Comparing Different Epsilons.mp4 | 8 MB | ||
| 6. Comparing Different Epsilons.srt | 5.3 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 | ||
| 6. Monte Carlo Control in Code.mp4 | 10.2 MB | ||
| 6. Monte Carlo Control in Code.srt | 5.8 KB | ||
| 6. Policy Iteration.mp4 | 3.1 MB | ||
| 6. Policy Iteration.srt | 3.5 KB | ||
| 6. Q Learning.mp4 | 4.8 MB | ||
| 6. Q Learning.srt | 5.8 KB | ||
| 6. TD(0) Semi-Gradient Prediction.mp4 | 8.4 MB | ||
| 6. TD(0) Semi-Gradient Prediction.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 | ||
| 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 | ||
| 7. Code pt 3.mp4 | 33.7 MB | ||
| 7. Code pt 3.srt | 5.4 KB | ||
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39 MB | ||
| 7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 31.8 KB | ||
| 7. Monte Carlo Control without Exploring Starts.mp4 | 4.6 MB | ||
| 7. Monte Carlo Control without Exploring Starts.srt | 5.5 KB | ||
| 7. Optimistic Initial Values.mp4 | 15.8 MB | ||
| 7. Optimistic Initial Values.srt | 3.1 KB | ||
| 7. Policy Iteration in Code.mp4 | 7.6 MB | ||
| 7. Policy Iteration in Code.srt | 6.1 KB | ||
| 7. Q Learning in Code.mp4 | 5.4 MB | ||
| 7. Q Learning in Code.srt | 3.5 KB | ||
| 7. Semi-Gradient SARSA.mp4 | 4.7 MB | ||
| 7. Semi-Gradient SARSA.srt | 5.5 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. Code pt 4.mp4 | 49.1 MB | ||
| 8. Code pt 4.srt | 8 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 | ||
| 8. Optimal Policy and Optimal Value Function.mp4 | 3.2 MB | ||
| 8. Optimal Policy and Optimal Value Function.srt | 5 KB | ||
| 8. Policy Iteration in Windy Gridworld.mp4 | 9.1 MB | ||
| 8. Policy Iteration in Windy Gridworld.srt | 8.2 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 | ||
| 8. Semi-Gradient SARSA in Code.mp4 | 10.6 MB | ||
| 8. Semi-Gradient SARSA in Code.srt | 5.4 KB | ||
| 8. TD Summary.mp4 | 3.9 MB | ||
| 8. TD Summary.srt | 4.7 KB | ||
| 8. Tic Tac Toe Code The Environment.mp4 | 10 MB | ||
| 8. Tic Tac Toe Code The Environment.srt | 12 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 | ||
| 9. Course Summary and Next Steps.mp4 | 13.2 MB | ||
| 9. Course Summary and Next Steps.srt | 16 KB | ||
| 9. MDP Summary.mp4 | 5.7 MB | ||
| 9. MDP Summary.srt | 2 KB | ||
| 9. Monte Carlo Summary.mp4 | 5.7 MB | ||
| 9. Monte Carlo Summary.srt | 7.1 KB | ||
| 9. Python 2 vs Python 3.mp4 | 7.8 MB | ||
| 9. Python 2 vs Python 3.srt | 6.1 KB | ||
| 9. Stock Trading Project Discussion.mp4 | 15.8 MB | ||
| 9. Stock Trading Project Discussion.srt | 4.3 KB | ||
| 9. Tic Tac Toe Code The Agent.mp4 | 9 MB | ||
| 9. Tic Tac Toe Code The Agent.srt | 10.9 KB | ||
| 9. Value Iteration.mp4 | 6.2 MB | ||
| 9. Value Iteration.srt | 7 KB | ||
| [GigaCourse.com].url | 0 B | ||
| ▲ 197 total files | |||
Udemy - Artificial Intelligence: Reinforcement Learning in Python
It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence. What’s covered in this course?
For more Udemy Courses: https://gigacourse.com
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.8 GB | freecoursewb | 2 weeks | 13 | 11 | |
| 2 GB | freecoursewb | 3 weeks | 27 | 34 | |
| 1.9 GB | freecoursewb | 1 month | 5 | 3 | |
| 2 GB | freecoursewb | 2 months | 15 | 8 | |
| 1.5 GB | freecoursewb | 2 months | 2 | 8 |
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