[Tutorialsplanet.NET] Udemy - Artificial Intelligence Reinforcement Learning in Python

Creation Time	June 15, 2024, 7:52 p.m.
Last Access Time	March 6, 2025, 6:15 p.m.
File Size	1.5 GB
Keywords	Tutorialsplanet Python Intelligence - Artificial Udemy Reinforcement Learning in NET
Total Requests	33
Total Files	177
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10. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 103.7 MB
5. Markov Decision Proccesses/7. Bellman Examples.mp4 87.1 MB
10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.mp4 54.6 MB
3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp4 51.8 MB
3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 51.2 MB
10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
2. High Level Overview of Reinforcement Learning and Course Outline/1. What is Reinforcement Learning.vtt 42.9 MB
2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.mp4 42.3 MB
10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
10. Appendix/11. What order should I take your courses in (part 2).mp4 37.6 MB
2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.mp4 37.1 MB
1. Welcome/1. Introduction.mp4 34.2 MB
2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.mp4 31.0 MB
10. Appendix/10. What order should I take your courses in (part 1).mp4 29.3 MB
10. Appendix/4. How to Code by Yourself (part 1).mp4 24.5 MB
3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp4 24.5 MB
6. Dynamic Programming/3. Designing Your RL Program.mp4 22.3 MB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp4 19.8 MB
5. Markov Decision Proccesses/5. Value Function Introduction.mp4 19.7 MB
10. Appendix/6. How to Succeed in this Course (Long Version).mp4 18.3 MB
10. Appendix/5. How to Code by Yourself (part 2).mp4 14.8 MB
9. Approximation Methods/9. Course Summary and Next Steps.mp4 13.2 MB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4 12.7 MB
6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp4 12.1 MB
6. Dynamic Programming/2. Gridworld in Code.mp4 11.5 MB
9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4 10.6 MB
3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 10.6 MB
7. Monte Carlo/6. Monte Carlo Control in Code.mp4 10.2 MB
4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp4 10.0 MB
4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4 9.8 MB
1. Welcome/3. Strategy for Passing the Course.mp4 9.5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4 9.4 MB
7. Monte Carlo/5. Monte Carlo Control.mp4 9.3 MB
6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp4 9.1 MB
4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp4 9.0 MB
8. Temporal Difference Learning/5. SARSA in Code.mp4 8.8 MB
7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 8.8 MB
9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4 8.4 MB
6. Dynamic Programming/11. Dynamic Programming Summary.mp4 8.3 MB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4 8.3 MB
5. Markov Decision Proccesses/6. Value Functions.mp4 8.3 MB
3. Return of the Multi-Armed Bandit/8. UCB1.mp4 8.2 MB
8. Temporal Difference Learning/4. SARSA.mp4 8.2 MB
7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4 8.1 MB
3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp4 8.0 MB
7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 7.9 MB
10. Appendix/9. Python 2 vs Python 3.mp4 7.8 MB
7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4 7.8 MB
6. Dynamic Programming/7. Policy Iteration in Code.mp4 7.6 MB
3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp4 7.5 MB
5. Markov Decision Proccesses/2. The Markov Property.mp4 7.2 MB
5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp4 6.6 MB
9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp4 6.6 MB
3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp4 6.5 MB
9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 6.5 MB
9. Approximation Methods/1. Approximation Intro.mp4 6.5 MB
9. Approximation Methods/3. Features.mp4 6.2 MB
6. Dynamic Programming/9. Value Iteration.mp4 6.2 MB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp4 6.1 MB
8. Temporal Difference Learning/2. TD(0) Prediction.mp4 5.8 MB
7. Monte Carlo/9. Monte Carlo Summary.mp4 5.7 MB
10. Appendix/1. What is the Appendix.mp4 5.5 MB
8. Temporal Difference Learning/7. Q Learning in Code.mp4 5.4 MB
8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 5.3 MB
5. Markov Decision Proccesses/4. Future Rewards.mp4 5.2 MB
3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp4 5.1 MB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp4 5.0 MB
7. Monte Carlo/1. Monte Carlo Intro.mp4 5.0 MB
6. Dynamic Programming/10. Value Iteration in Code.mp4 4.9 MB
8. Temporal Difference Learning/6. Q Learning.mp4 4.8 MB
6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 4.8 MB
9. Approximation Methods/7. Semi-Gradient SARSA.mp4 4.7 MB
7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp4 4.6 MB
6. Dynamic Programming/5. Policy Improvement.mp4 4.5 MB
1. Welcome/2. Where to get the Code.mp4 4.5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp4 4.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp4 4.2 MB
10. Appendix/12. Where to get discount coupons and FREE deep learning material.mp4 4.0 MB
8. Temporal Difference Learning/8. TD Summary.mp4 3.9 MB
5. Markov Decision Proccesses/1. Gridworld.mp4 3.4 MB
5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp4 3.2 MB
6. Dynamic Programming/6. Policy Iteration.mp4 3.1 MB
9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp4 2.8 MB
3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp4 2.8 MB
8. Temporal Difference Learning/1. Temporal Difference Intro.mp4 2.7 MB
5. Markov Decision Proccesses/9. MDP Summary.mp4 2.4 MB
3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp4 2.2 MB
10. Appendix/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 29.9 KB
10. Appendix/4. How to Code by Yourself (part 1).vtt 27.3 KB
5. Markov Decision Proccesses/7. Bellman Examples.vtt 25.8 KB
10. Appendix/11. What order should I take your courses in (part 2).vtt 22.3 KB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.vtt 21.7 KB
10. Appendix/2. Windows-Focused Environment Setup 2018.vtt 18.9 KB
10. Appendix/5. How to Code by Yourself (part 2).vtt 16.7 KB
10. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 16.6 KB
10. Appendix/10. What order should I take your courses in (part 1).vtt 15.2 KB
5. Markov Decision Proccesses/5. Value Function Introduction.vtt 14.5 KB
9. Approximation Methods/9. Course Summary and Next Steps.vtt 14.5 KB
10. Appendix/6. How to Succeed in this Course (Long Version).vtt 13.7 KB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.vtt 13.4 KB
10. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 13.2 KB
3. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.vtt 11.0 KB
5. Markov Decision Proccesses/6. Value Functions.vtt 11.0 KB
4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.vtt 10.9 KB
1. Welcome/3. Strategy for Passing the Course.vtt 10.7 KB
4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.vtt 10.3 KB
3. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.vtt 10.3 KB
6. Dynamic Programming/2. Gridworld in Code.vtt 10.0 KB
4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.vtt 10.0 KB
7. Monte Carlo/2. Monte Carlo Policy Evaluation.vtt 9.8 KB
7. Monte Carlo/5. Monte Carlo Control.vtt 9.3 KB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.vtt 9.3 KB
6. Dynamic Programming/4. Iterative Policy Evaluation in Code.vtt 9.3 KB
8. Temporal Difference Learning/4. SARSA.vtt 8.9 KB
2. High Level Overview of Reinforcement Learning and Course Outline/4. Defining Some Terms.vtt 8.7 KB
6. Dynamic Programming/11. Dynamic Programming Summary.vtt 8.6 KB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.vtt 8.4 KB
5. Markov Decision Proccesses/2. The Markov Property.vtt 7.7 KB
2. High Level Overview of Reinforcement Learning and Course Outline/2. On Unusual or Unexpected Strategies of RL.vtt 7.5 KB
6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.vtt 7.5 KB
3. Return of the Multi-Armed Bandit/8. UCB1.vtt 7.4 KB
9. Approximation Methods/1. Approximation Intro.vtt 7.3 KB
5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.vtt 7.2 KB
3. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.vtt 7.1 KB
3. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.vtt 7.1 KB
9. Approximation Methods/2. Linear Models for Reinforcement Learning.vtt 6.8 KB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.vtt 6.6 KB
7. Monte Carlo/9. Monte Carlo Summary.vtt 6.5 KB
6. Dynamic Programming/9. Value Iteration.vtt 6.4 KB
9. Approximation Methods/3. Features.vtt 6.3 KB
6. Dynamic Programming/3. Designing Your RL Program.vtt 6.2 KB
2. High Level Overview of Reinforcement Learning and Course Outline/3. Course Outline.vtt 6.1 KB
10. Appendix/9. Python 2 vs Python 3.vtt 5.9 KB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.vtt 5.9 KB
8. Temporal Difference Learning/2. TD(0) Prediction.vtt 5.8 KB
9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.vtt 5.8 KB
7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.vtt 5.6 KB
6. Dynamic Programming/7. Policy Iteration in Code.vtt 5.6 KB
3. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.vtt 5.5 KB
5. Markov Decision Proccesses/4. Future Rewards.vtt 5.5 KB
7. Monte Carlo/1. Monte Carlo Intro.vtt 5.4 KB
3. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.vtt 5.4 KB
8. Temporal Difference Learning/6. Q Learning.vtt 5.4 KB
7. Monte Carlo/6. Monte Carlo Control in Code.vtt 5.3 KB
8. Temporal Difference Learning/5. SARSA in Code.vtt 5.0 KB
7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.vtt 5.0 KB
9. Approximation Methods/7. Semi-Gradient SARSA.vtt 5.0 KB
9. Approximation Methods/8. Semi-Gradient SARSA in Code.vtt 4.9 KB
1. Welcome/2. Where to get the Code.vtt 4.9 KB
6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.vtt 4.9 KB
3. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.vtt 4.9 KB
7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.vtt 4.9 KB
6. Dynamic Programming/5. Policy Improvement.vtt 4.7 KB
5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.vtt 4.7 KB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.vtt 4.5 KB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.vtt 4.5 KB
8. Temporal Difference Learning/8. TD Summary.vtt 4.3 KB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.vtt 4.0 KB
1. Welcome/1. Introduction.vtt 3.9 KB
5. Markov Decision Proccesses/1. Gridworld.vtt 3.7 KB
9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.vtt 3.7 KB
8. Temporal Difference Learning/3. TD(0) Prediction in Code.vtt 3.6 KB
10. Appendix/1. What is the Appendix.vtt 3.4 KB
7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.vtt 3.3 KB
10. Appendix/12. Where to get discount coupons and FREE deep learning material.vtt 3.3 KB
6. Dynamic Programming/6. Policy Iteration.vtt 3.2 KB
8. Temporal Difference Learning/7. Q Learning in Code.vtt 3.1 KB
8. Temporal Difference Learning/1. Temporal Difference Intro.vtt 3.1 KB
6. Dynamic Programming/10. Value Iteration in Code.vtt 3.0 KB
3. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.vtt 3.0 KB
3. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.vtt 2.9 KB
5. Markov Decision Proccesses/9. MDP Summary.vtt 2.4 KB
9. Approximation Methods/4. Monte Carlo Prediction with Approximation.vtt 2.2 KB
3. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.vtt 2.0 KB
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[Tutorialsplanet.NET] Udemy - Artificial Intelligence Reinforcement Learning in Python

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