首页 磁力链接怎么用

Modern Reinforcement Learning- Deep Q Learning in PyTorch

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2020-8-28 21:17 2024-12-20 08:14 185 2.36 GB 40
磁力链接
magnet:?xt=urn:btih:99ef61813ac987ad003d48d82fd91f1aa9098f2c
迅雷链接
thunder://QUFtYWduZXQ6P3h0PXVybjpidGloOjk5ZWY2MTgxM2FjOTg3YWQwMDNkNDhkODJmZDkxZjFhYTkwOThmMmNaWg==
二维码链接
Modern Reinforcement Learning- Deep Q Learning in PyTorch的二维码
种子下载(838888不存储任何种子文件)
种子下载线路1(迅雷)--推荐
种子下载线路2(比特彗星)
种子下载线路3(torcache)
3条线路均为国内外知名下载网站种子链接，内容跟本站无关！
相关链接

Modern Reinforcement Learning- Deep Q Learning in PyTorch

文件列表
  1. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/018 Analyzing the Paper.mp4279.17MB
  2. Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/001 What You Will Learn In This Course.mp429.02MB
  3. Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/002 Required Background software and hardware.mp423.68MB
  4. Modern Reinforcement Learning- Deep Q Learning in PyTorch/01 Introduction/003 How to Succeed in this Course.mp4105.19MB
  5. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/004 Agents Environments and Actions.mp457.77MB
  6. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/005 Markov Decision Processes.mp460.48MB
  7. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/006 Value Functions Action Value Functions and the Bellman Equation.mp447.17MB
  8. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/007 Model Free vs. Model Based Learning.mp425.29MB
  9. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/008 The Explore-Exploit Dilemma.mp437.87MB
  10. Modern Reinforcement Learning- Deep Q Learning in PyTorch/02 Fundamentals of Reinforcement Learning/009 Temporal Difference Learning.mp4129.49MB
  11. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/010 Dealing with Continuous State Spaces with Deep Neural Networks.mp4105.27MB
  12. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/011 Naive Deep Q Learning in Code Step 1 - Coding the Deep Q Network.mp444.16MB
  13. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/012 Naive Deep Q Learning in Code Step 2 - Coding the Agent Class.mp460.14MB
  14. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/013 Naive Deep Q Learning in Code Step 3 - Coding the Main Loop and Learning.mp445.75MB
  15. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/014 Naive Deep Q Learning in Code Step 4 - Verifying the Functionality of Our Code.mp418.73MB
  16. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/015 Naive Deep Q Learning in Code Step 5 - Analyzing Our Agents Performance.mp418.92MB
  17. Modern Reinforcement Learning- Deep Q Learning in PyTorch/03 Deep Learning Crash Course/016 Dealing with Screen Images with Convolutional Neural Networks.mp419.76MB
  18. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/017 How to Read Deep Learning Papers.mp449.72MB
  19. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/019 How to Modify the OpenAI Gym Atari Environments.mp481.79MB
  20. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/020 How to Preprocess the OpenAI Gym Atari Screen Images.mp418.55MB
  21. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/021 How to Stack the Preprocessed Atari Screen Images.mp424.53MB
  22. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/022 How to Combine All the Changes.mp49.23MB
  23. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/023 How to Add Reward Clipping Fire First and No Ops.mp430.62MB
  24. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/024 How to Code the Agents Memory.mp461.4MB
  25. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/025 How to Code the Deep Q Network.mp466.44MB
  26. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/026 Coding the Deep Q Agent Step 1 - Coding the Constructor.mp439.81MB
  27. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/027 Coding the Deep Q Agent Step 2 - Epsilon-Greedy Action Selection.mp415.26MB
  28. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/028 Coding the Deep Q Agent Step 3 - Memory Model Saving and Network Copying.mp431.1MB
  29. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/029 Coding the Deep Q Agent Step 4 - The Agents Learn Function.mp438MB
  30. Modern Reinforcement Learning- Deep Q Learning in PyTorch/04 Human Level Control Through Deep Reinforcement Learning From Paper to Code/030 Coding the Deep Q Agent Step 5 - The Main Loop and Analyzing the Performance.mp472.96MB
  31. Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/031 Analyzing the Paper.mp4182.66MB
  32. Modern Reinforcement Learning- Deep Q Learning in PyTorch/05 Deep Reinforcement Learning with Double Q Learning/032 Coding the Double Q Learning Agent and Analyzing Performance.mp458.28MB
  33. Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/033 Analyzing the Paper.mp4133.99MB
  34. Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/034 Coding the Dueling Deep Q Network.mp423.55MB
  35. Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/035 Coding the Dueling Deep Q Learning Agent and Analyzing Performance.mp470.57MB
  36. Modern Reinforcement Learning- Deep Q Learning in PyTorch/06 Dueling Network Architectures for Deep Reinforcement Learning/036 Coding the Dueling Double Deep Q Learning Agent and Analyzing Performance.mp437.26MB
  37. Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/037 Implementing a Command Line Interface for Rapid Model Testing.mp457.15MB
  38. Modern Reinforcement Learning- Deep Q Learning in PyTorch/07 Improving On Our Solutions/038 Consolidating Our Code Base for Maximum Extensability.mp4168.75MB
  39. Modern Reinforcement Learning- Deep Q Learning in PyTorch/08 Conclusion/039 Summarizing What Weve Learned.mp435.46MB
  40. Modern Reinforcement Learning- Deep Q Learning in PyTorch/09 Bonus Lecture/040 Bonus Video Where to Go From Here.mp45.95MB
友情提示
不会用的朋友看这里 把磁力链接复制到离线下载，或者bt下载软件里即可下载文件，或者直接复制迅雷链接到迅雷里下载！ 亲，你造吗？将网页分享给您的基友，下载的人越多速度越快哦！

违规内容投诉邮箱：[email protected]

概述 838888磁力搜索是一个磁力链接搜索引擎，是学术研究的副产品，用于解决资源过度分散的问题 它通过BitTorrent协议加入DHT网络，实时的自动采集数据，仅存储文件的标题、大小、文件列表、文件标识符（磁力链接）等基础信息 838888磁力搜索不下载任何真实资源，无法判断资源的合法性及真实性，使用838888磁力搜索服务的用户需自行鉴别内容的真伪 838888磁力搜索不上传任何资源，不提供Tracker服务，不提供种子文件的下载，这意味着838888磁力搜索 838888磁力搜索是一个完全合法的系统
838888磁力链接 磁力链接bt种子搜索引擎磁力搜索 DMCA