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

1. 1. Welcome/1. Introduction.mp429.55MB
2. 1. Welcome/2. Outline.mp454.25MB
3. 1. Welcome/3. Where to get the code.mp424.45MB
4. 2. Review of Fundamental Reinforcement Learning Concepts/1. Review Section Introduction.mp418.88MB
5. 2. Review of Fundamental Reinforcement Learning Concepts/2. The Explore-Exploit Dilemma.mp471.63MB
6. 2. Review of Fundamental Reinforcement Learning Concepts/3. Markov Decision Processes (MDPs).mp4108.66MB
7. 2. Review of Fundamental Reinforcement Learning Concepts/4. Monte Carlo Methods.mp432.07MB
8. 2. Review of Fundamental Reinforcement Learning Concepts/5. Temporal Difference Learning (TD).mp478.57MB
9. 2. Review of Fundamental Reinforcement Learning Concepts/6. OpenAI Gym Warmup.mp449.72MB
10. 2. Review of Fundamental Reinforcement Learning Concepts/7. Review Section Summary.mp431.17MB
11. 3. A2C (Advantage Actor-Critic)/1. A2C Section Introduction.mp461.3MB
12. 3. A2C (Advantage Actor-Critic)/10. A2C.mp4192.28MB
13. 3. A2C (Advantage Actor-Critic)/11. A2C Section Summary.mp432.72MB
14. 3. A2C (Advantage Actor-Critic)/2. A2C Theory (part 1).mp496.21MB
15. 3. A2C (Advantage Actor-Critic)/3. A2C Theory (part 2).mp432.59MB
16. 3. A2C (Advantage Actor-Critic)/4. A2C Theory (part 3).mp414.22MB
17. 3. A2C (Advantage Actor-Critic)/5. A2C Demo.mp427.42MB
18. 3. A2C (Advantage Actor-Critic)/6. A2C Code - Rough Sketch.mp428.49MB
19. 3. A2C (Advantage Actor-Critic)/7. Multiple Processes.mp470.09MB
20. 3. A2C (Advantage Actor-Critic)/8. Environment Wrappers.mp4128.58MB
21. 3. A2C (Advantage Actor-Critic)/9. Convolutional Neural Network.mp445.66MB
22. 4. DDPG (Deep Deterministic Policy Gradient)/1. DDPG Section Introduction.mp423.92MB
23. 4. DDPG (Deep Deterministic Policy Gradient)/2. Deep Q-Learning (DQN) Review.mp445.16MB
24. 4. DDPG (Deep Deterministic Policy Gradient)/3. DDPG Theory.mp480.68MB
25. 4. DDPG (Deep Deterministic Policy Gradient)/4. MuJoCo.mp4110.45MB
26. 4. DDPG (Deep Deterministic Policy Gradient)/5. DDPG Code (part 1).mp4193.58MB
27. 4. DDPG (Deep Deterministic Policy Gradient)/6. DDPG Code (part 2).mp464.82MB
28. 4. DDPG (Deep Deterministic Policy Gradient)/7. DDPG Section Summary.mp417.6MB
29. 5. ES (Evolution Strategies)/1. ES Section Introduction.mp444.86MB
30. 5. ES (Evolution Strategies)/2. ES Theory.mp4108.21MB
31. 5. ES (Evolution Strategies)/3. Notes on Evolution Strategies.mp453.1MB
32. 5. ES (Evolution Strategies)/4. ES for Optimizing a Function.mp446.51MB
33. 5. ES (Evolution Strategies)/5. ES for Supervised Learning.mp455.16MB
34. 5. ES (Evolution Strategies)/6. Flappy Bird.mp460.92MB
35. 5. ES (Evolution Strategies)/7. ES for Flappy Bird in Code.mp4142.23MB
36. 5. ES (Evolution Strategies)/8. ES for MuJoCo in Code.mp468.63MB
37. 5. ES (Evolution Strategies)/9. ES Section Summary.mp428.64MB
38. 6. Appendix FAQ/1. What is the Appendix.mp418.07MB
39. 6. Appendix FAQ/10. What order should I take your courses in (part 1).mp499.39MB
40. 6. Appendix FAQ/11. What order should I take your courses in (part 2).mp4139.37MB
41. 6. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4194.34MB
42. 6. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4167.01MB
43. 6. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.54MB
44. 6. Appendix FAQ/5. How to Succeed in this Course (Long Version).mp439.26MB
45. 6. Appendix FAQ/6. How to Code by Yourself (part 1).mp482.57MB
46. 6. Appendix FAQ/7. How to Code by Yourself (part 2).mp456.7MB
47. 6. Appendix FAQ/8. Proof that using Jupyter Notebook is the same as not using it.mp478.27MB
48. 6. Appendix FAQ/9. Python 2 vs Python 3.mp418.98MB