Udemy - Cutting-Edge AI Deep Reinforcement Learning in Python (10.2022)

磁力链接/BT种子名称

Udemy - Cutting-Edge AI Deep Reinforcement Learning in Python (10.2022)

磁力链接/BT种子简介

种子哈希：d228ab9cfb87b2e263b4dc55da51d066b7f308a1
文件大小： 1.97G
已经下载：172次
下载速度：极快
收录时间：2025-07-09
最近下载：2025-10-08

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磁力链接

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文件列表

  • 06 - Setting Up Your Environment (FAQ by Student Request)/001 Anaconda Environment Setup.mp4 183.7 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/005 DDPG Code (part 1).mp4 130.3 MB
  • 03 - A2C (Advantage Actor-Critic)/010 A2C.mp4 122.6 MB
  • 06 - Setting Up Your Environment (FAQ by Student Request)/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 114.3 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/004 MuJoCo.mp4 98.0 MB
  • 05 - ES (Evolution Strategies)/007 ES for Flappy Bird in Code.mp4 95.8 MB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 85.2 MB
  • 03 - A2C (Advantage Actor-Critic)/008 Environment Wrappers.mp4 84.5 MB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.4 MB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/003 Proof that using Jupyter Notebook is the same as not using it.mp4 67.5 MB
  • 03 - A2C (Advantage Actor-Critic)/001 A2C Section Introduction.mp4 59.6 MB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/001 How to Code by Yourself (part 1).mp4 58.8 MB
  • 01 - Welcome/002 Outline.mp4 53.7 MB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 53.3 MB
  • 03 - A2C (Advantage Actor-Critic)/002 A2C Theory (part 1).mp4 51.6 MB
  • 03 - A2C (Advantage Actor-Critic)/007 Multiple Processes.mp4 46.8 MB
  • 05 - ES (Evolution Strategies)/002 ES Theory.mp4 45.9 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/006 DDPG Code (part 2).mp4 40.9 MB
  • 09 - Appendix FAQ Finale/002 BONUS Lecture.mp4 39.6 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/003 Markov Decision Processes (MDPs).mp4 38.0 MB
  • 05 - ES (Evolution Strategies)/005 ES for Supervised Learning.mp4 37.6 MB
  • 01 - Welcome/001 Introduction.mp4 37.5 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/003 DDPG Theory.mp4 33.3 MB
  • 05 - ES (Evolution Strategies)/008 ES for MuJoCo in Code.mp4 31.8 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/005 Temporal Difference Learning (TD).mp4 31.8 MB
  • 03 - A2C (Advantage Actor-Critic)/009 Convolutional Neural Network.mp4 31.0 MB
  • 05 - ES (Evolution Strategies)/006 Flappy Bird.mp4 26.5 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/002 The Explore-Exploit Dilemma.mp4 26.3 MB
  • 05 - ES (Evolution Strategies)/003 Notes on Evolution Strategies.mp4 26.0 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/008 Suggestion Box.mp4 24.4 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/002 Deep Q-Learning (DQN) Review.mp4 23.3 MB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/002 How to Code by Yourself (part 2).mp4 21.9 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/006 OpenAI Gym Warmup.mp4 21.9 MB
  • 05 - ES (Evolution Strategies)/004 ES for Optimizing a Function.mp4 20.2 MB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/001 How to Succeed in this Course (Long Version).mp4 17.6 MB
  • 03 - A2C (Advantage Actor-Critic)/005 A2C Demo.mp4 17.2 MB
  • 05 - ES (Evolution Strategies)/001 ES Section Introduction.mp4 14.9 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/004 Monte Carlo Methods.mp4 13.4 MB
  • 03 - A2C (Advantage Actor-Critic)/003 A2C Theory (part 2).mp4 12.9 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/007 Review Section Summary.mp4 12.5 MB
  • 03 - A2C (Advantage Actor-Critic)/011 A2C Section Summary.mp4 12.2 MB
  • 03 - A2C (Advantage Actor-Critic)/006 A2C Code - Rough Sketch.mp4 11.7 MB
  • 05 - ES (Evolution Strategies)/009 ES Section Summary.mp4 10.6 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/001 DDPG Section Introduction.mp4 9.9 MB
  • 01 - Welcome/003 Where to get the code.mp4 9.0 MB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/004 Python 2 vs Python 3.mp4 8.0 MB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/001 Review Section Introduction.mp4 7.2 MB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/007 DDPG Section Summary.mp4 7.2 MB
  • 03 - A2C (Advantage Actor-Critic)/004 A2C Theory (part 3).mp4 6.5 MB
  • 09 - Appendix FAQ Finale/001 What is the Appendix.mp4 6.4 MB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/002 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.7 kB
  • 03 - A2C (Advantage Actor-Critic)/002 A2C Theory (part 1).srt 26.8 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/003 Markov Decision Processes (MDPs).srt 26.5 kB
  • 05 - ES (Evolution Strategies)/002 ES Theory.srt 26.2 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/004 MuJoCo.srt 24.7 kB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/004 Machine Learning and AI Prerequisite Roadmap (pt 2).srt 24.1 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/005 DDPG Code (part 1).srt 23.6 kB
  • 03 - A2C (Advantage Actor-Critic)/010 A2C.srt 23.5 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/003 DDPG Theory.srt 23.4 kB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/001 How to Code by Yourself (part 1).srt 23.2 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/005 Temporal Difference Learning (TD).srt 22.2 kB
  • 06 - Setting Up Your Environment (FAQ by Student Request)/001 Anaconda Environment Setup.srt 20.2 kB
  • 05 - ES (Evolution Strategies)/007 ES for Flappy Bird in Code.srt 18.2 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/002 The Explore-Exploit Dilemma.srt 18.1 kB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/003 Machine Learning and AI Prerequisite Roadmap (pt 1).srt 17.2 kB
  • 05 - ES (Evolution Strategies)/006 Flappy Bird.srt 15.8 kB
  • 03 - A2C (Advantage Actor-Critic)/008 Environment Wrappers.srt 15.5 kB
  • 08 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/001 How to Succeed in this Course (Long Version).srt 15.1 kB
  • 06 - Setting Up Your Environment (FAQ by Student Request)/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.5 kB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/003 Proof that using Jupyter Notebook is the same as not using it.srt 14.4 kB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/002 How to Code by Yourself (part 2).srt 13.5 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/002 Deep Q-Learning (DQN) Review.srt 12.2 kB
  • 05 - ES (Evolution Strategies)/003 Notes on Evolution Strategies.srt 11.8 kB
  • 03 - A2C (Advantage Actor-Critic)/007 Multiple Processes.srt 10.9 kB
  • 01 - Welcome/002 Outline.srt 10.8 kB
  • 03 - A2C (Advantage Actor-Critic)/001 A2C Section Introduction.srt 10.7 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/004 Monte Carlo Methods.srt 10.0 kB
  • 03 - A2C (Advantage Actor-Critic)/006 A2C Code - Rough Sketch.srt 9.8 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/007 Review Section Summary.srt 9.5 kB
  • 05 - ES (Evolution Strategies)/008 ES for MuJoCo in Code.srt 9.3 kB
  • 03 - A2C (Advantage Actor-Critic)/003 A2C Theory (part 2).srt 9.2 kB
  • 05 - ES (Evolution Strategies)/001 ES Section Introduction.srt 8.9 kB
  • 03 - A2C (Advantage Actor-Critic)/011 A2C Section Summary.srt 8.8 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/006 OpenAI Gym Warmup.srt 8.4 kB
  • 09 - Appendix FAQ Finale/002 BONUS Lecture.srt 8.1 kB
  • 05 - ES (Evolution Strategies)/005 ES for Supervised Learning.srt 7.7 kB
  • 05 - ES (Evolution Strategies)/004 ES for Optimizing a Function.srt 7.7 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/006 DDPG Code (part 2).srt 7.1 kB
  • 03 - A2C (Advantage Actor-Critic)/009 Convolutional Neural Network.srt 7.0 kB
  • 01 - Welcome/003 Where to get the code.srt 6.5 kB
  • 05 - ES (Evolution Strategies)/009 ES Section Summary.srt 6.4 kB
  • 07 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/004 Python 2 vs Python 3.srt 6.2 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/007 DDPG Section Summary.srt 5.3 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/001 Review Section Introduction.srt 5.2 kB
  • 01 - Welcome/001 Introduction.srt 5.2 kB
  • 02 - Review of Fundamental Reinforcement Learning Concepts/008 Suggestion Box.srt 4.9 kB
  • 04 - DDPG (Deep Deterministic Policy Gradient)/001 DDPG Section Introduction.srt 4.5 kB
  • 03 - A2C (Advantage Actor-Critic)/004 A2C Theory (part 3).srt 3.9 kB
  • 09 - Appendix FAQ Finale/001 What is the Appendix.srt 3.9 kB
  • 03 - A2C (Advantage Actor-Critic)/005 A2C Demo.srt 2.9 kB
  • 01 - Welcome/003 Github-Link.url 83 Bytes
  • 01 - Welcome/external-links.txt 80 Bytes

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