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[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
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[GigaCourse.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
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收录时间:
2021-04-13
最近下载:
2024-12-31
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文件列表
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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