| 1. Approximation Intro.mp4 | 6.5 MB | ||
| 1. Approximation Intro.vtt | 6.5 MB | ||
| 1. Gridworld.mp4 | 3.4 MB | ||
| 1. Gridworld.vtt | 3.7 KB | ||
| 1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 | 4.8 MB | ||
| 1. Intro to Dynamic Programming and Iterative Policy Evaluation.vtt | 4.9 KB | ||
| 1. Introduction and outline.mp4 | 10.1 MB | ||
| 1. Introduction and outline.vtt | 12 KB | ||
| 1. Monte Carlo Intro.mp4 | 5 MB | ||
| 1. Monte Carlo Intro.vtt | 5.4 KB | ||
| 1. Naive Solution to Tic-Tac-Toe.mp4 | 6.1 MB | ||
| 1. Naive Solution to Tic-Tac-Toe.vtt | 6.6 KB | ||
| 1. Problem Setup and The Explore-Exploit Dilemma.mp4 | 6.5 MB | ||
| 1. Problem Setup and The Explore-Exploit Dilemma.vtt | 7.1 KB | ||
| 1. Temporal Difference Intro.mp4 | 2.7 MB | ||
| 1. Temporal Difference Intro.vtt | 3.1 KB | ||
| 1. What is the Appendix.mp4 | 5.5 MB | ||
| 1. What is the Appendix.vtt | 3.4 KB | ||
| 10. Dynamic Programming Summary.mp4 | 8.3 MB | ||
| 10. Dynamic Programming Summary.vtt | 8.6 KB | ||
| 10. Tic Tac Toe Code Main Loop and Demo.mp4 | 9.4 MB | ||
| 10. Tic Tac Toe Code Main Loop and Demo.vtt | 8.4 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).vtt | 15.2 KB | ||
| 11. Tic Tac Toe Summary.mp4 | 8.3 MB | ||
| 11. Tic Tac Toe Summary.vtt | 9.3 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).vtt | 22.3 KB | ||
| 12. Where to get discount coupons and FREE deep learning material.mp4 | 4 MB | ||
| 12. Where to get discount coupons and FREE deep learning material.vtt | 3.3 KB | ||
| 2. Components of a Reinforcement Learning System.mp4 | 12.7 MB | ||
| 2. Components of a Reinforcement Learning System.vtt | 13.4 KB | ||
| 2. Epsilon-Greedy.mp4 | 2.8 MB | ||
| 2. Epsilon-Greedy.vtt | 2.9 KB | ||
| 2. Gridworld in Code.mp4 | 11.5 MB | ||
| 2. Gridworld in Code.vtt | 10 KB | ||
| 2. Linear Models for Reinforcement Learning.mp4 | 6.5 MB | ||
| 2. Linear Models for Reinforcement Learning.vtt | 6.8 KB | ||
| 2. Monte Carlo Policy Evaluation.mp4 | 8.8 MB | ||
| 2. Monte Carlo Policy Evaluation.vtt | 9.8 KB | ||
| 2. TD(0) Prediction.mp4 | 5.8 MB | ||
| 2. TD(0) Prediction.vtt | 5.8 KB | ||
| 2. The Markov Property.mp4 | 7.2 MB | ||
| 2. The Markov Property.vtt | 7.7 KB | ||
| 2. What is Reinforcement Learning.mp4 | 22 MB | ||
| 2. What is Reinforcement Learning.vtt | 24 KB | ||
| 2. Windows-Focused Environment Setup 2018.mp4 | 186.4 MB | ||
| 2. Windows-Focused Environment Setup 2018.vtt | 18.9 KB | ||
| 3. Defining and Formalizing the MDP.mp4 | 6.6 MB | ||
| 3. Defining and Formalizing the MDP.vtt | 7.2 KB | ||
| 3. Features.mp4 | 6.3 MB | ||
| 3. Features.vtt | 6.3 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.vtt | 16.6 KB | ||
| 3. Iterative Policy Evaluation in Code.mp4 | 12.1 MB | ||
| 3. Iterative Policy Evaluation in Code.vtt | 9.3 KB | ||
| 3. Monte Carlo Policy Evaluation in Code.mp4 | 7.9 MB | ||
| 3. Monte Carlo Policy Evaluation in Code.vtt | 5.6 KB | ||
| 3. Notes on Assigning Rewards.mp4 | 4.2 MB | ||
| 3. Notes on Assigning Rewards.vtt | 4.5 KB | ||
| 3. TD(0) Prediction in Code.mp4 | 5.3 MB | ||
| 3. TD(0) Prediction in Code.vtt | 3.6 KB | ||
| 3. Updating a Sample Mean.mp4 | 2.2 MB | ||
| 3. Updating a Sample Mean.vtt | 2 KB | ||
| 3. Where to get the Code.mp4 | 4.5 MB | ||
| 3. Where to get the Code.vtt | 4.9 KB | ||
| 4. Comparing Different Epsilons.mp4 | 8 MB | ||
| 4. Comparing Different Epsilons.vtt | 4.9 KB | ||
| 4. Future Rewards.mp4 | 5.2 MB | ||
| 4. Future Rewards.vtt | 5.5 KB | ||
| 4. How to Code by Yourself (part 1).mp4 | 24.5 MB | ||
| 4. How to Code by Yourself (part 1).vtt | 27.3 KB | ||
| 4. Monte Carlo Prediction with Approximation.mp4 | 2.8 MB | ||
| 4. Monte Carlo Prediction with Approximation.vtt | 2.2 KB | ||
| 4. Policy Evaluation in Windy Gridworld.mp4 | 7.8 MB | ||
| 4. Policy Evaluation in Windy Gridworld.vtt | 4.9 KB | ||
| 4. Policy Improvement.mp4 | 4.5 MB | ||
| 4. Policy Improvement.vtt | 4.7 KB | ||
| 4. SARSA.mp4 | 8.2 MB | ||
| 4. SARSA.vtt | 8.9 KB | ||
| 4. Strategy for Passing the Course.mp4 | 9.5 MB | ||
| 4. Strategy for Passing the Course.vtt | 10.7 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.vtt | 21.7 KB | ||
| 5. How to Code by Yourself (part 2).mp4 | 14.8 MB | ||
| 5. How to Code by Yourself (part 2).vtt | 16.7 KB | ||
| 5. Monte Carlo Control.mp4 | 9.3 MB | ||
| 5. Monte Carlo Control.vtt | 9.3 KB | ||
| 5. Monte Carlo Prediction with Approximation in Code.mp4 | 6.6 MB | ||
| 5. Monte Carlo Prediction with Approximation in Code.vtt | 3.7 KB | ||
| 5. Optimistic Initial Values.mp4 | 5.1 MB | ||
| 5. Optimistic Initial Values.vtt | 3 KB | ||
| 5. Policy Iteration.mp4 | 3.1 MB | ||
| 5. Policy Iteration.vtt | 3.2 KB | ||
| 5. SARSA in Code.mp4 | 8.8 MB | ||
| 5. SARSA in Code.vtt | 5 KB | ||
| 5. Tic Tac Toe Code Outline.mp4 | 5 MB | ||
| 5. Tic Tac Toe Code Outline.vtt | 5.9 KB | ||
| 5. Value Function Introduction.mp4 | 19.7 MB | ||
| 5. Value Function Introduction.vtt | 14.5 KB | ||
| 6. How to Succeed in this Course (Long Version).mp4 | 18.3 MB | ||
| 6. How to Succeed in this Course (Long Version).vtt | 13.7 KB | ||
| 6. Monte Carlo Control in Code.mp4 | 10.2 MB | ||
| 6. Monte Carlo Control in Code.vtt | 5.3 KB | ||
| 6. Policy Iteration in Code.mp4 | 7.6 MB | ||
| 6. Policy Iteration in Code.vtt | 5.6 KB | ||
| 6. Q Learning.mp4 | 4.8 MB | ||
| 6. Q Learning.vtt | 5.4 KB | ||
| 6. TD(0) Semi-Gradient Prediction.mp4 | 8.3 MB | ||
| 6. TD(0) Semi-Gradient Prediction.vtt | 5.8 KB | ||
| 6. Tic Tac Toe Code Representing States.mp4 | 4.4 MB | ||
| 6. Tic Tac Toe Code Representing States.vtt | 4.5 KB | ||
| 6. UCB1.mp4 | 8.2 MB | ||
| 6. UCB1.vtt | 7.4 KB | ||
| 6. Value Functions.mp4 | 8.3 MB | ||
| 6. Value Functions.vtt | 11 KB | ||
| 7. Bayesian Thompson Sampling.mp4 | 51.8 MB | ||
| 7. Bayesian Thompson Sampling.vtt | 11 KB | ||
| 7. Bellman Examples.mp4 | 87.1 MB | ||
| 7. Bellman Examples.vtt | 25.8 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.vtt | 29.9 KB | ||
| 7. Monte Carlo Control without Exploring Starts.mp4 | 4.6 MB | ||
| 7. Monte Carlo Control without Exploring Starts.vtt | 5 KB | ||
| 7. Policy Iteration in Windy Gridworld.mp4 | 9.1 MB | ||
| 7. Policy Iteration in Windy Gridworld.vtt | 7.5 KB | ||
| 7. Q Learning in Code.mp4 | 5.4 MB | ||
| 7. Q Learning in Code.vtt | 3.1 KB | ||
| 7. Semi-Gradient SARSA.mp4 | 4.7 MB | ||
| 7. Semi-Gradient SARSA.vtt | 5 KB | ||
| 7. Tic Tac Toe Code Enumerating States Recursively.mp4 | 9.8 MB | ||
| 7. Tic Tac Toe Code Enumerating States Recursively.vtt | 10.3 KB | ||
| 8. Monte Carlo Control without Exploring Starts in Code.mp4 | 8.1 MB | ||
| 8. Monte Carlo Control without Exploring Starts in Code.vtt | 3.3 KB | ||
| 8. Optimal Policy and Optimal Value Function.mp4 | 3.2 MB | ||
| 8. Optimal Policy and Optimal Value Function.vtt | 4.7 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.vtt | 13.2 KB | ||
| 8. Semi-Gradient SARSA in Code.mp4 | 10.6 MB | ||
| 8. Semi-Gradient SARSA in Code.vtt | 4.9 KB | ||
| 8. TD Summary.mp4 | 3.9 MB | ||
| 8. TD Summary.vtt | 4.3 KB | ||
| 8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 | 10.6 MB | ||
| 8. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.vtt | 5.5 KB | ||
| 8. Tic Tac Toe Code The Environment.mp4 | 10 MB | ||
| 8. Tic Tac Toe Code The Environment.vtt | 10.9 KB | ||
| 8. Value Iteration.mp4 | 6.2 MB | ||
| 8. Value Iteration.vtt | 6.4 KB | ||
| 9. Course Summary and Next Steps.mp4 | 13.2 MB | ||
| 9. Course Summary and Next Steps.vtt | 14.5 KB | ||
| 9. MDP Summary.mp4 | 2.4 MB | ||
| 9. MDP Summary.vtt | 2.4 KB | ||
| 9. Monte Carlo Summary.mp4 | 5.7 MB | ||
| 9. Monte Carlo Summary.vtt | 6.5 KB | ||
| 9. Nonstationary Bandits.mp4 | 7.5 MB | ||
| 9. Nonstationary Bandits.vtt | 7.1 KB | ||
| 9. Python 2 vs Python 3.mp4 | 7.8 MB | ||
| 9. Python 2 vs Python 3.vtt | 5.9 KB | ||
| 9. Tic Tac Toe Code The Agent.mp4 | 9 MB | ||
| 9. Tic Tac Toe Code The Agent.vtt | 10 KB | ||
| 9. Value Iteration in Code.mp4 | 4.9 MB | ||
| 9. Value Iteration in Code.vtt | 3 KB | ||
| [CourseClub.NET].url | 102.4 B | ||
| [FCS Forum].url | 102.4 B | ||
| [FreeCourseSite.com].url | 102.4 B | ||
| ▲ 165 total files | |||
Udemy - Artificial Intelligence Reinforcement Learning in Python
The complete guide to artificial intelligence and machine learning, prep for deep reinforcement learning.
Created by Lazy Programmer Inc.
Last updated 1/2019
For more Udemy Courses: https://freecoursesite.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 |
All Comments