DHT 元数据搜索 最新 视频 音乐 电子书 文档 图片 Robots

Artificial Intelligence Reinforcement Learning in Python

Artificial Intelligence Reinforcement Learning in Python metadata, size 4282.45 MB, 330 files, category video, indexed at 2026-08-01.

大小：4.2 GB 格式：mp4 分类：视频 文件数量：330 个 发掘时间：2026-08-02 03:45:35 最后活跃：2026-08-02 03:45:35

资源标识

Infohash7b1c7e7b41e24c586b33e26d247d5c0ebf1e589e 总大小4.2 GB 格式mp4 分类视频 文件数330 发掘时间（北京时间）2026-08-02 03:45:35 最后活跃（北京时间）2026-08-02 03:45:35

下载标识

完整磁力链接 magnet:?xt=urn:btih:7b1c7e7b41e24c586b33e26d247d5c0ebf1e589e&dn=Artificial%20Intelligence%20Reinforcement%20Learning%20in%20Python
迅雷地址 thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjdiMWM3ZTdiNDFlMjRjNTg2YjMzZTI2ZDI0N2Q1YzBlYmYxZTU4OWUmZG49QXJ0aWZpY2lhbCUyMEludGVsbGlnZW5jZSUyMFJlaW5mb3JjZW1lbnQlMjBMZWFybmluZyUyMGluJTIwUHl0aG9uWlo=
电驴地址

不能由磁力 infohash 直接转换。ed2k 需要文件内容 MD4 hash、文件名和文件大小，BitTorrent infohash 与 ed2k hash 不是同一种算法。

Tag

videomp4inlearningpythonartificialintelligencereinforcement

文件列表

文件名格式大小
1. Windows-Focused Environment Setup 2018.mp4mp4186.4 MB
TutsNode.com.txttxt63 B
11. Bellman Examples-en_US.srtsrt26.6 KB
1. How to Code by Yourself (part 1)-en_US.srtsrt26.0 KB
16. Bayesian Bandits Thompson Sampling Theory (pt 2)-en_US.srtsrt22.7 KB
4. Machine Learning and AI Prerequisite Roadmap (pt 2)-en_US.srtsrt22.2 KB
3. External URLs.txttxt75 B
2. Iterative Policy Evaluation-en_US.srtsrt20.4 KB
1. Beginners, halt! Stop here if you skipped ahead-en_US.srtsrt19.9 KB
1. Windows-Focused Environment Setup 2018-en_US.srtsrt19.3 KB
12. UCB1 Theory-en_US.srtsrt19.2 KB
5. Markov Decision Processes (MDPs)-en_US.srtsrt18.8 KB
5. Warmup-en_US.srtsrt18.1 KB
2. Gridworld-en_US.srtsrt16.6 KB
15. Bayesian Bandits Thompson Sampling Theory (pt 1)-en_US.srtsrt16.1 KB
2. How to Code by Yourself (part 2)-en_US.srtsrt15.8 KB
4. Gridworld in Code-en_US.srtsrt15.7 KB
2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow-en_US.srtsrt15.7 KB
5. Iterative Policy Evaluation in Code-en_US.srtsrt15.6 KB
3. Machine Learning and AI Prerequisite Roadmap (pt 1)-en_US.srtsrt15.4 KB
3. Data and Environment-en_US.srtsrt15.1 KB
19. Thompson Sampling With Gaussian Reward Theory-en_US.srtsrt14.4 KB
8. Policy Improvement-en_US.srtsrt14.2 KB
2. Monte Carlo Policy Evaluation-en_US.srtsrt14.1 KB
1. How to Succeed in this Course (Long Version)-en_US.srtsrt14.0 KB
24. (Optional) Alternative Bandit Designs-en_US.srtsrt13.9 KB
3. Feature Engineering-en_US.srtsrt13.9 KB
3. Proof that using Jupyter Notebook is the same as not using it-en_US.srtsrt13.5 KB
1. Section Introduction The Explore-Exploit Dilemma-en_US.srtsrt13.0 KB
6. Future Rewards-en_US.srtsrt12.2 KB
1. Monte Carlo Intro-en_US.srtsrt12.1 KB
10. Optimistic Initial Values Beginner's Exercise Prompt-en_US.srtsrt2.8 KB
4. Approximation Methods for Prediction-en_US.srtsrt12.1 KB
1. Dynamic Programming Section Introduction-en_US.srtsrt11.9 KB
2. From Bandits to Full Reinforcement Learning-en_US.srtsrt11.6 KB
4. How to Model Q for Q-Learning-en_US.srtsrt11.6 KB
7. Code pt 2-en_US.srtsrt11.3 KB
13. UCB1 Beginner's Exercise Prompt-en_US.srtsrt2.6 KB
4. Monte Carlo Control-en_US.srtsrt11.2 KB
12. Optimal Policy and Optimal Value Function (pt 1)-en_US.srtsrt11.0 KB
2. Linear Models for Reinforcement Learning-en_US.srtsrt11.0 KB
5. Monte Carlo Control in Code-en_US.srtsrt10.7 KB
8. The Bellman Equation (pt 1)-en_US.srtsrt10.7 KB
11. Policy Iteration in Windy Gridworld-en_US.srtsrt10.6 KB
7. Approximation Methods for Control Code-en_US.srtsrt10.5 KB
1. What is Reinforcement Learning-en_US.srtsrt10.5 KB
10. The Bellman Equation (pt 3)-en_US.srtsrt7.4 KB
11. Bellman Examples.mp4mp487.1 MB
2. Applications of the Explore-Exploit Dilemma-en_US.srtsrt10.5 KB
10. Policy Iteration in Code-en_US.srtsrt10.4 KB

合规说明

本站只展示公开 DHT metadata，不提供实际文件下载、种子下载、在线播放、镜像或代理。投诉请邮件联系 dmca@so.yy.la，并附上详情页 URL 或 infohash。