[FreeTutorials.Us] [UDEMY] Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]

磁力链接/BT种子名称

[FreeTutorials.Us] [UDEMY] Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]

磁力链接/BT种子简介

种子哈希：9d445aaf0fff5ffe5c45caafd66e692781faf725
文件大小： 3.31G
已经下载：4922次
下载速度：极快
收录时间：2020-03-21
最近下载：2025-10-08

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文件列表

  • 6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 203.8 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4 203.0 MB
  • 3. A2C (Advantage Actor-Critic)/10. A2C.mp4 201.6 MB
  • 6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 175.1 MB
  • 5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4 149.1 MB
  • 6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 146.1 MB
  • 3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4 134.8 MB
  • 6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 123.2 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4 115.8 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4 113.9 MB
  • 5. ES (Evolution Strategies)/2. ES Theory.mp4 113.5 MB
  • 6. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 104.2 MB
  • 3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp4 100.9 MB
  • 6. Appendix FAQ/6. How to Code by Yourself (part 1).mp4 86.6 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp4 84.6 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp4 82.4 MB
  • 6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp4 75.1 MB
  • 3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp4 73.5 MB
  • 5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp4 72.0 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp4 68.0 MB
  • 3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp4 64.3 MB
  • 5. ES (Evolution Strategies)/6. Flappy Bird.mp4 63.9 MB
  • 6. Appendix FAQ/7. How to Code by Yourself (part 2).mp4 59.5 MB
  • 5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp4 57.8 MB
  • 1. Welcome/2. Outline.mp4 56.9 MB
  • 5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp4 55.7 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp4 52.1 MB
  • 5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp4 48.8 MB
  • 3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp4 47.9 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp4 47.4 MB
  • 5. ES (Evolution Strategies)/1. ES Section Introduction.mp4 47.0 MB
  • 6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp4 41.2 MB
  • 3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp4 34.3 MB
  • 3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp4 34.2 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp4 33.6 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp4 32.7 MB
  • 1. Welcome/1. Introduction.mp4 31.0 MB
  • 5. ES (Evolution Strategies)/9. ES Section Summary.mp4 30.0 MB
  • 3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp4 29.9 MB
  • 3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp4 28.8 MB
  • 1. Welcome/3. Where to get the code.mp4 25.6 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp4 25.1 MB
  • 6. Appendix FAQ/9. Python 2 vs Python 3.mp4 19.9 MB
  • 2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp4 19.8 MB
  • 6. Appendix FAQ/1. What is the Appendix.mp4 18.9 MB
  • 4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp4 18.5 MB
  • 3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp4 14.9 MB
  • 6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28.3 kB
  • 3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).vtt 23.3 kB
  • 5. ES (Evolution Strategies)/2. ES Theory.vtt 22.9 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).vtt 22.8 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.vtt 21.6 kB
  • 6. Appendix FAQ/11. What order should I take your courses in (part 2).vtt 20.7 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.vtt 20.5 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).vtt 20.5 kB
  • 3. A2C (Advantage Actor-Critic)/10. A2C.vtt 20.2 kB
  • 6. Appendix FAQ/6. How to Code by Yourself (part 1).vtt 19.8 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).vtt 19.0 kB
  • 6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt 17.8 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.vtt 15.9 kB
  • 5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.vtt 15.9 kB
  • 6. Appendix FAQ/10. What order should I take your courses in (part 1).vtt 14.5 kB
  • 5. ES (Evolution Strategies)/6. Flappy Bird.vtt 14.1 kB
  • 3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.vtt 13.6 kB
  • 6. Appendix FAQ/5. How to Succeed in this Course (Long Version).vtt 13.1 kB
  • 6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.9 kB
  • 6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.vtt 12.6 kB
  • 6. Appendix FAQ/7. How to Code by Yourself (part 2).vtt 11.7 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.vtt 10.7 kB
  • 5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.vtt 10.3 kB
  • 3. A2C (Advantage Actor-Critic)/7. Multiple Processes.vtt 9.8 kB
  • 1. Welcome/2. Outline.vtt 9.5 kB
  • 3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.vtt 9.4 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.vtt 8.7 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.vtt 8.5 kB
  • 3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.vtt 8.3 kB
  • 5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.vtt 8.3 kB
  • 3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).vtt 8.1 kB
  • 3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.vtt 8.0 kB
  • 5. ES (Evolution Strategies)/1. ES Section Introduction.vtt 7.7 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.vtt 7.4 kB
  • 5. ES (Evolution Strategies)/4. ES for Optimizing a Function.vtt 6.9 kB
  • 5. ES (Evolution Strategies)/5. ES for Supervised Learning.vtt 6.8 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).vtt 6.3 kB
  • 3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.vtt 6.2 kB
  • 5. ES (Evolution Strategies)/9. ES Section Summary.vtt 5.8 kB
  • 1. Welcome/3. Where to get the code.vtt 5.7 kB
  • 6. Appendix FAQ/9. Python 2 vs Python 3.vtt 5.5 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.vtt 4.8 kB
  • 2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.vtt 4.7 kB
  • 1. Welcome/1. Introduction.vtt 4.5 kB
  • 4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.vtt 4.0 kB
  • 3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).vtt 3.5 kB
  • 6. Appendix FAQ/1. What is the Appendix.vtt 3.4 kB
  • 3. A2C (Advantage Actor-Critic)/5. A2C Demo.vtt 2.5 kB
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  • 0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url 239 Bytes
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