[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python - 磁力管家

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文档列表

11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 195.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.mp4 108.8 MB
5. Markov Decision Proccesses/7. Bellman Examples.mp4 91.3 MB
11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.mp4 68.5 MB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.mp4 57.3 MB
10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.mp4 54.5 MB
2. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.mp4 54.4 MB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.mp4 53.7 MB
10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.mp4 52.1 MB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.mp4 51.5 MB
10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.mp4 47.1 MB
11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.mp4 44.4 MB
11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.mp4 39.7 MB
11. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 39.4 MB
3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.mp4 38.9 MB
1. Welcome/1. Introduction.mp4 35.9 MB
2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.mp4 35.6 MB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.mp4 35.4 MB
1. Welcome/4. Course Outline.mp4 32.5 MB
11. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 30.7 MB
10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.mp4 28.1 MB
11. Appendix FAQ/4. How to Code by Yourself (part 1).mp4 25.7 MB
2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.mp4 25.7 MB
10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.mp4 24.4 MB
6. Dynamic Programming/3. Designing Your RL Program.mp4 23.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.mp4 20.7 MB
5. Markov Decision Proccesses/5. Value Function Introduction.mp4 20.7 MB
11. Appendix FAQ/6. How to Succeed in this Course (Long Version).mp4 19.2 MB
2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.mp4 16.6 MB
10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.mp4 16.5 MB
11. Appendix FAQ/5. How to Code by Yourself (part 2).mp4 15.5 MB
9. Approximation Methods/9. Course Summary and Next Steps.mp4 13.9 MB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.mp4 13.3 MB
6. Dynamic Programming/4. Iterative Policy Evaluation in Code.mp4 12.6 MB
6. Dynamic Programming/2. Gridworld in Code.mp4 12.0 MB
9. Approximation Methods/8. Semi-Gradient SARSA in Code.mp4 11.1 MB
2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp4 11.1 MB
7. Monte Carlo/6. Monte Carlo Control in Code.mp4 10.7 MB
4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.mp4 10.5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.mp4 10.3 MB
1. Welcome/3. Strategy for Passing the Course.mp4 9.9 MB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.mp4 9.9 MB
7. Monte Carlo/5. Monte Carlo Control.mp4 9.7 MB
6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.mp4 9.5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.mp4 9.4 MB
8. Temporal Difference Learning/5. SARSA in Code.mp4 9.2 MB
7. Monte Carlo/2. Monte Carlo Policy Evaluation.mp4 9.2 MB
9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.mp4 8.8 MB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.mp4 8.7 MB
6. Dynamic Programming/11. Dynamic Programming Summary.mp4 8.7 MB
5. Markov Decision Proccesses/6. Value Functions.mp4 8.7 MB
2. Return of the Multi-Armed Bandit/8. UCB1.mp4 8.6 MB
8. Temporal Difference Learning/4. SARSA.mp4 8.6 MB
7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.mp4 8.4 MB
2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.mp4 8.4 MB
7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.mp4 8.3 MB
11. Appendix FAQ/9. Python 2 vs Python 3.mp4 8.2 MB
7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.mp4 8.2 MB
6. Dynamic Programming/7. Policy Iteration in Code.mp4 8.0 MB
2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.mp4 7.8 MB
5. Markov Decision Proccesses/2. The Markov Property.mp4 7.5 MB
5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.mp4 7.0 MB
9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.mp4 6.9 MB
2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.mp4 6.8 MB
9. Approximation Methods/2. Linear Models for Reinforcement Learning.mp4 6.8 MB
9. Approximation Methods/1. Approximation Intro.mp4 6.8 MB
9. Approximation Methods/3. Features.mp4 6.6 MB
6. Dynamic Programming/9. Value Iteration.mp4 6.5 MB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.mp4 6.4 MB
8. Temporal Difference Learning/2. TD(0) Prediction.mp4 6.1 MB
7. Monte Carlo/9. Monte Carlo Summary.mp4 6.0 MB
5. Markov Decision Proccesses/9. MDP Summary.mp4 5.9 MB
11. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
8. Temporal Difference Learning/7. Q Learning in Code.mp4 5.7 MB
8. Temporal Difference Learning/3. TD(0) Prediction in Code.mp4 5.6 MB
5. Markov Decision Proccesses/4. Future Rewards.mp4 5.4 MB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.mp4 5.3 MB
7. Monte Carlo/1. Monte Carlo Intro.mp4 5.2 MB
6. Dynamic Programming/10. Value Iteration in Code.mp4 5.1 MB
8. Temporal Difference Learning/6. Q Learning.mp4 5.1 MB
6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.mp4 5.1 MB
9. Approximation Methods/7. Semi-Gradient SARSA.mp4 4.9 MB
7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.mp4 4.8 MB
6. Dynamic Programming/5. Policy Improvement.mp4 4.8 MB
1. Welcome/2. Where to get the Code.mp4 4.7 MB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.mp4 4.6 MB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.mp4 4.4 MB
8. Temporal Difference Learning/8. TD Summary.mp4 4.1 MB
5. Markov Decision Proccesses/1. Gridworld.mp4 3.5 MB
5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.mp4 3.4 MB
6. Dynamic Programming/6. Policy Iteration.mp4 3.3 MB
9. Approximation Methods/4. Monte Carlo Prediction with Approximation.mp4 3.0 MB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.mp4 2.9 MB
8. Temporal Difference Learning/1. Temporal Difference Intro.mp4 2.9 MB
2. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.mp4 2.3 MB
11. Appendix FAQ/7. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.5 kB
11. Appendix FAQ/4. How to Code by Yourself (part 1).srt 30.9 kB
5. Markov Decision Proccesses/7. Bellman Examples.srt 28.3 kB
11. Appendix FAQ/11. What order should I take your courses in (part 2).srt 23.6 kB
4. Build an Intelligent Tic-Tac-Toe Agent/4. The Value Function and Your First Reinforcement Learning Algorithm.srt 23.3 kB
11. Appendix FAQ/2. Windows-Focused Environment Setup 2018.srt 20.6 kB
11. Appendix FAQ/5. How to Code by Yourself (part 2).srt 18.9 kB
11. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 18.8 kB
11. Appendix FAQ/10. What order should I take your courses in (part 1).srt 16.4 kB
9. Approximation Methods/9. Course Summary and Next Steps.srt 16.3 kB
10. Stock Trading Project with Reinforcement Learning/2. Data and Environment.srt 16.1 kB
5. Markov Decision Proccesses/5. Value Function Introduction.srt 16.0 kB
4. Build an Intelligent Tic-Tac-Toe Agent/2. Components of a Reinforcement Learning System.srt 15.2 kB
11. Appendix FAQ/6. How to Succeed in this Course (Long Version).srt 14.9 kB
11. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
10. Stock Trading Project with Reinforcement Learning/3. How to Model Q for Q-Learning.srt 12.3 kB
4. Build an Intelligent Tic-Tac-Toe Agent/8. Tic Tac Toe Code The Environment.srt 12.3 kB
2. Return of the Multi-Armed Bandit/9. Bayesian Thompson Sampling.srt 12.1 kB
1. Welcome/3. Strategy for Passing the Course.srt 12.1 kB
5. Markov Decision Proccesses/6. Value Functions.srt 12.0 kB
10. Stock Trading Project with Reinforcement Learning/6. Code pt 2.srt 12.0 kB
4. Build an Intelligent Tic-Tac-Toe Agent/7. Tic Tac Toe Code Enumerating States Recursively.srt 11.6 kB
6. Dynamic Programming/2. Gridworld in Code.srt 11.2 kB
4. Build an Intelligent Tic-Tac-Toe Agent/9. Tic Tac Toe Code The Agent.srt 11.2 kB
2. Return of the Multi-Armed Bandit/2. Applications of the Explore-Exploit Dilemma.srt 11.2 kB
3. High Level Overview of Reinforcement Learning/1. What is Reinforcement Learning.srt 11.2 kB
7. Monte Carlo/2. Monte Carlo Policy Evaluation.srt 11.1 kB
7. Monte Carlo/5. Monte Carlo Control.srt 10.5 kB
6. Dynamic Programming/4. Iterative Policy Evaluation in Code.srt 10.5 kB
4. Build an Intelligent Tic-Tac-Toe Agent/11. Tic Tac Toe Summary.srt 10.5 kB
8. Temporal Difference Learning/4. SARSA.srt 9.9 kB
10. Stock Trading Project with Reinforcement Learning/5. Code pt 1.srt 9.9 kB
6. Dynamic Programming/11. Dynamic Programming Summary.srt 9.6 kB
4. Build an Intelligent Tic-Tac-Toe Agent/10. Tic Tac Toe Code Main Loop and Demo.srt 9.5 kB
3. High Level Overview of Reinforcement Learning/3. Defining Some Terms.srt 9.4 kB
2. Return of the Multi-Armed Bandit/12. Bandit Summary, Real Data, and Online Learning.srt 9.3 kB
10. Stock Trading Project with Reinforcement Learning/4. Design of the Program.srt 8.7 kB
5. Markov Decision Proccesses/2. The Markov Property.srt 8.6 kB
6. Dynamic Programming/8. Policy Iteration in Windy Gridworld.srt 8.4 kB
2. Return of the Multi-Armed Bandit/8. UCB1.srt 8.3 kB
10. Stock Trading Project with Reinforcement Learning/8. Code pt 4.srt 8.2 kB
9. Approximation Methods/1. Approximation Intro.srt 8.2 kB
3. High Level Overview of Reinforcement Learning/2. On Unusual or Unexpected Strategies of RL.srt 8.1 kB
11. Appendix FAQ/12. BONUS Where to get discount coupons and FREE deep learning material.srt 8.1 kB
5. Markov Decision Proccesses/3. Defining and Formalizing the MDP.srt 8.1 kB
2. Return of the Multi-Armed Bandit/1. Problem Setup and The Explore-Exploit Dilemma.srt 8.0 kB
2. Return of the Multi-Armed Bandit/11. Nonstationary Bandits.srt 8.0 kB
9. Approximation Methods/2. Linear Models for Reinforcement Learning.srt 7.6 kB
4. Build an Intelligent Tic-Tac-Toe Agent/1. Naive Solution to Tic-Tac-Toe.srt 7.4 kB
7. Monte Carlo/9. Monte Carlo Summary.srt 7.3 kB
6. Dynamic Programming/9. Value Iteration.srt 7.1 kB
9. Approximation Methods/3. Features.srt 7.1 kB
10. Stock Trading Project with Reinforcement Learning/1. Stock Trading Project Section Introduction.srt 7.0 kB
1. Welcome/4. Course Outline.srt 7.0 kB
6. Dynamic Programming/3. Designing Your RL Program.srt 6.8 kB
4. Build an Intelligent Tic-Tac-Toe Agent/5. Tic Tac Toe Code Outline.srt 6.6 kB
8. Temporal Difference Learning/2. TD(0) Prediction.srt 6.5 kB
9. Approximation Methods/6. TD(0) Semi-Gradient Prediction.srt 6.5 kB
7. Monte Carlo/3. Monte Carlo Policy Evaluation in Code.srt 6.3 kB
11. Appendix FAQ/9. Python 2 vs Python 3.srt 6.2 kB
2. Return of the Multi-Armed Bandit/10. Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.srt 6.2 kB
6. Dynamic Programming/7. Policy Iteration in Code.srt 6.2 kB
5. Markov Decision Proccesses/4. Future Rewards.srt 6.2 kB
7. Monte Carlo/1. Monte Carlo Intro.srt 6.1 kB
7. Monte Carlo/6. Monte Carlo Control in Code.srt 6.0 kB
8. Temporal Difference Learning/6. Q Learning.srt 6.0 kB
2. Return of the Multi-Armed Bandit/5. Designing Your Bandit Program.srt 5.7 kB
8. Temporal Difference Learning/5. SARSA in Code.srt 5.7 kB
7. Monte Carlo/7. Monte Carlo Control without Exploring Starts.srt 5.7 kB
9. Approximation Methods/7. Semi-Gradient SARSA.srt 5.6 kB
10. Stock Trading Project with Reinforcement Learning/7. Code pt 3.srt 5.5 kB
1. Welcome/2. Where to get the Code.srt 5.5 kB
9. Approximation Methods/8. Semi-Gradient SARSA in Code.srt 5.5 kB
6. Dynamic Programming/1. Intro to Dynamic Programming and Iterative Policy Evaluation.srt 5.5 kB
2. Return of the Multi-Armed Bandit/6. Comparing Different Epsilons.srt 5.4 kB
7. Monte Carlo/4. Policy Evaluation in Windy Gridworld.srt 5.4 kB
6. Dynamic Programming/5. Policy Improvement.srt 5.3 kB
5. Markov Decision Proccesses/8. Optimal Policy and Optimal Value Function.srt 5.1 kB
4. Build an Intelligent Tic-Tac-Toe Agent/3. Notes on Assigning Rewards.srt 5.1 kB
4. Build an Intelligent Tic-Tac-Toe Agent/6. Tic Tac Toe Code Representing States.srt 5.0 kB
8. Temporal Difference Learning/8. TD Summary.srt 4.8 kB
4. Build an Intelligent Tic-Tac-Toe Agent/12. Tic Tac Toe Exercise.srt 4.7 kB
10. Stock Trading Project with Reinforcement Learning/9. Stock Trading Project Discussion.srt 4.5 kB
1. Welcome/1. Introduction.srt 4.3 kB
5. Markov Decision Proccesses/1. Gridworld.srt 4.1 kB
9. Approximation Methods/5. Monte Carlo Prediction with Approximation in Code.srt 4.1 kB
8. Temporal Difference Learning/3. TD(0) Prediction in Code.srt 4.1 kB
11. Appendix FAQ/1. What is the Appendix.srt 3.8 kB
7. Monte Carlo/8. Monte Carlo Control without Exploring Starts in Code.srt 3.7 kB
6. Dynamic Programming/6. Policy Iteration.srt 3.5 kB
8. Temporal Difference Learning/7. Q Learning in Code.srt 3.5 kB
6. Dynamic Programming/10. Value Iteration in Code.srt 3.4 kB
8. Temporal Difference Learning/1. Temporal Difference Intro.srt 3.4 kB
2. Return of the Multi-Armed Bandit/3. Epsilon-Greedy.srt 3.3 kB
2. Return of the Multi-Armed Bandit/7. Optimistic Initial Values.srt 3.1 kB
9. Approximation Methods/4. Monte Carlo Prediction with Approximation.srt 2.4 kB
2. Return of the Multi-Armed Bandit/4. Updating a Sample Mean.srt 2.2 kB
5. Markov Decision Proccesses/9. MDP Summary.srt 2.0 kB
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