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种子名称： [FreeTutorials.Us] [UDEMY] Cutting-Edge AI Deep Reinforcement Learning in Python [FTU]
文件类型： 视频
文件数目： 48个文件
文件大小： 3.31 GB
收录时间： 2019-9-16 23:20
已经下载： 3次
资源热度： 214
最近下载： 2024-5-10 21:22

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

1. Welcome/1. Introduction.mp429.55MB

1. Welcome/2. Outline.mp454.25MB

1. Welcome/3. Where to get the code.mp424.45MB

2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp418.88MB

2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp471.63MB

2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4108.66MB

2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp432.07MB

2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp478.57MB

2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp449.72MB

2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp431.17MB

3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp461.3MB

3. A2C (Advantage Actor-Critic)/10. A2C.mp4192.28MB

3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp432.72MB

3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp496.21MB

3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp432.59MB

3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp414.22MB

3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp427.42MB

3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp428.49MB

3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp470.09MB

3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4128.58MB

3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp445.66MB

4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp423.92MB

4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp445.16MB

4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp480.68MB

4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4110.45MB

4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4193.58MB

4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp464.82MB

4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp417.6MB

5. ES (Evolution Strategies)/1. ES Section Introduction.mp444.86MB

5. ES (Evolution Strategies)/2. ES Theory.mp4108.21MB

5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp453.1MB

5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp446.51MB

5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp455.16MB

5. ES (Evolution Strategies)/6. Flappy Bird.mp460.92MB

5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4142.23MB

5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp468.63MB

5. ES (Evolution Strategies)/9. ES Section Summary.mp428.64MB

6. Appendix FAQ/1. What is the Appendix.mp418.07MB

6. Appendix FAQ/10. What order should I take your courses in (part 1).mp499.39MB

6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4139.37MB

6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4194.34MB

6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4167.01MB

6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.54MB

6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp439.26MB

6. Appendix FAQ/6. How to Code by Yourself (part 1).mp482.57MB

6. Appendix FAQ/7. How to Code by Yourself (part 2).mp456.7MB

6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp478.27MB

6. Appendix FAQ/9. Python 2 vs Python 3.mp418.98MB