种子简介
种子名称:
[FreeCourseSite.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
文件类型:
视频
文件数目:
71个文件
文件大小:
592.52 MB
收录时间:
2018-3-4 18:21
已经下载:
3次
资源热度:
199
最近下载:
2024-12-27 19:12
下载BT种子文件
下载Torrent文件(.torrent)
立即下载
磁力链接下载
magnet:?xt=urn:btih:23814b5e3b355ce8da598b08b02dc855ceeb3160&dn=[FreeCourseSite.com] Udemy - Artificial Intelligence Reinforcement Learning in Python
复制链接到迅雷、QQ旋风进行下载,或者使用百度云离线下载。
喜欢这个种子的人也喜欢
种子包含的文件
[FreeCourseSite.com] Udemy - Artificial Intelligence Reinforcement Learning in Python.torrent
01 Introduction and Outline/001 Introduction and outline.mp410.1MB
01 Introduction and Outline/002 What is Reinforcement Learning.mp421.94MB
01 Introduction and Outline/003 Where to get the Code.mp44.45MB
01 Introduction and Outline/004 Strategy for Passing the Course.mp49.47MB
02 Return of the Multi-Armed Bandit/005 Problem Setup and The Explore-Exploit Dilemma.mp46.47MB
02 Return of the Multi-Armed Bandit/006 Epsilon-Greedy.mp42.78MB
02 Return of the Multi-Armed Bandit/007 Updating a Sample Mean.mp42.17MB
02 Return of the Multi-Armed Bandit/008 Comparing Different Epsilons.mp48.01MB
02 Return of the Multi-Armed Bandit/009 Optimistic Initial Values.mp45.12MB
02 Return of the Multi-Armed Bandit/010 UCB1.mp48.23MB
02 Return of the Multi-Armed Bandit/011 Bayesian Thompson Sampling.mp415.23MB
02 Return of the Multi-Armed Bandit/012 Thompson Sampling vs. Epsilon-Greedy vs. Optimistic Initial Values vs. UCB1.mp410.57MB
02 Return of the Multi-Armed Bandit/013 Nonstationary Bandits.mp47.48MB
03 Build an Intelligent Tic-Tac-Toe Agent/014 Naive Solution to Tic-Tac-Toe.mp46.11MB
03 Build an Intelligent Tic-Tac-Toe Agent/015 Components of a Reinforcement Learning System.mp412.71MB
03 Build an Intelligent Tic-Tac-Toe Agent/016 Notes on Assigning Rewards.mp44.22MB
03 Build an Intelligent Tic-Tac-Toe Agent/017 The Value Function and Your First Reinforcement Learning Algorithm.mp426.13MB
03 Build an Intelligent Tic-Tac-Toe Agent/018 Tic Tac Toe Code Outline.mp45.03MB
03 Build an Intelligent Tic-Tac-Toe Agent/019 Tic Tac Toe Code Representing States.mp44.42MB
03 Build an Intelligent Tic-Tac-Toe Agent/020 Tic Tac Toe Code Enumerating States Recursively.mp49.79MB
03 Build an Intelligent Tic-Tac-Toe Agent/021 Tic Tac Toe Code The Environment.mp410.05MB
03 Build an Intelligent Tic-Tac-Toe Agent/022 Tic Tac Toe Code The Agent.mp49.01MB
03 Build an Intelligent Tic-Tac-Toe Agent/023 Tic Tac Toe Code Main Loop and Demo.mp49.44MB
03 Build an Intelligent Tic-Tac-Toe Agent/024 Tic Tac Toe Summary.mp48.31MB
04 Markov Decision Proccesses/025 Gridworld.mp43.36MB
04 Markov Decision Proccesses/026 The Markov Property.mp47.18MB
04 Markov Decision Proccesses/027 Defining and Formalizing the MDP.mp46.64MB
04 Markov Decision Proccesses/028 Future Rewards.mp45.17MB
04 Markov Decision Proccesses/029 Value Functions.mp47.08MB
04 Markov Decision Proccesses/030 Optimal Policy and Optimal Value Function.mp46.31MB
04 Markov Decision Proccesses/031 MDP Summary.mp42.41MB
05 Dynamic Programming/032 Intro to Dynamic Programming and Iterative Policy Evaluation.mp44.83MB
05 Dynamic Programming/033 Gridworld in Code.mp411.46MB
05 Dynamic Programming/034 Iterative Policy Evaluation in Code.mp412.06MB
05 Dynamic Programming/035 Policy Improvement.mp44.53MB
05 Dynamic Programming/036 Policy Iteration.mp43.13MB
05 Dynamic Programming/037 Policy Iteration in Code.mp47.62MB
05 Dynamic Programming/038 Policy Iteration in Windy Gridworld.mp49.1MB
05 Dynamic Programming/039 Value Iteration.mp46.18MB
05 Dynamic Programming/040 Value Iteration in Code.mp44.89MB
05 Dynamic Programming/041 Dynamic Programming Summary.mp48.31MB
06 Monte Carlo/042 Monte Carlo Intro.mp44.97MB
06 Monte Carlo/043 Monte Carlo Policy Evaluation.mp48.75MB
06 Monte Carlo/044 Monte Carlo Policy Evaluation in Code.mp47.91MB
06 Monte Carlo/045 Policy Evaluation in Windy Gridworld.mp47.81MB
06 Monte Carlo/046 Monte Carlo Control.mp49.26MB
06 Monte Carlo/047 Monte Carlo Control in Code.mp410.17MB
06 Monte Carlo/048 Monte Carlo Control without Exploring Starts.mp44.62MB
06 Monte Carlo/049 Monte Carlo Control without Exploring Starts in Code.mp48.05MB
06 Monte Carlo/050 Monte Carlo Summary.mp45.71MB
07 Temporal Difference Learning/051 Temporal Difference Intro.mp42.72MB
07 Temporal Difference Learning/052 TD0 Prediction.mp45.82MB
07 Temporal Difference Learning/053 TD0 Prediction in Code.mp45.32MB
07 Temporal Difference Learning/054 SARSA.mp48.2MB
07 Temporal Difference Learning/055 SARSA in Code.mp48.82MB
07 Temporal Difference Learning/056 Q Learning.mp44.84MB
07 Temporal Difference Learning/057 Q Learning in Code.mp45.42MB
07 Temporal Difference Learning/058 TD Summary.mp43.94MB
08 Approximation Methods/059 Approximation Intro.mp46.46MB
08 Approximation Methods/060 Linear Models for Reinforcement Learning.mp46.46MB
08 Approximation Methods/061 Features.mp46.24MB
08 Approximation Methods/062 Monte Carlo Prediction with Approximation.mp42.84MB
08 Approximation Methods/063 Monte Carlo Prediction with Approximation in Code.mp46.56MB
08 Approximation Methods/064 TD0 Semi-Gradient Prediction.mp48.35MB
08 Approximation Methods/065 Semi-Gradient SARSA.mp44.7MB
08 Approximation Methods/066 Semi-Gradient SARSA in Code.mp410.61MB
08 Approximation Methods/067 Course Summary and Next Steps.mp413.24MB
09 Appendix/068 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp443.92MB
09 Appendix/069 How to Code by Yourself part 1.mp424.53MB
09 Appendix/070 How to Code by Yourself part 2.mp414.8MB
09 Appendix/071 Where to get discount coupons and FREE deep learning material.mp44.02MB