Deep Reinforcement Learning 2.0

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[ FreeCourseWeb.com ] Udemy - Deep Reinforcement Learning 2.0
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Part 1 - Fundamentals
    • 1. Welcome-en_US.srt (23.4 KB)
    • 1. Welcome.mp4 (123.8 MB)
    • 2. Q-Learning-en_US.srt (15.4 KB)
    • 2. Q-Learning.mp4 (81.5 MB)
    • 3. Deep Q-Learning-en_US.srt (9.5 KB)
    • 3. Deep Q-Learning.mp4 (34.7 MB)
    • 4. Policy Gradient-en_US.srt (9.0 KB)
    • 4. Policy Gradient.mp4 (60.0 MB)
    • 5. Actor-Critic-en_US.srt (5.5 KB)
    • 5. Actor-Critic.mp4 (24.9 MB)
    • 6. Taxonomy of AI models-en_US.srt (10.5 KB)
    • 6. Taxonomy of AI models.mp4 (28.8 MB)
    • BONUS 5 Advantages of DRL.html (1.5 KB)
    • BONUS Learning Path.html (1.4 KB)
    • BONUS RL Algorithms Map.html (1.1 KB)
    • Get the materials.html (0.3 KB)
    • Some resources before we start.html (1.0 KB)
    2. Part 2 - Twin Delayed DDPG Theory
    • 1. Introduction and Initialization-en_US.srt (20.2 KB)
    • 1. Introduction and Initialization.mp4 (82.6 MB)
    • 2. The Q-Learning part-en_US.srt (26.7 KB)
    • 2. The Q-Learning part.mp4 (136.0 MB)
    • 3. The Policy Learning part-en_US.srt (18.8 KB)
    • 3. The Policy Learning part.mp4 (268.6 MB)
    • 4. The whole training process-en_US.srt (5.0 KB)
    • 4. The whole training process.mp4 (21.5 MB)
    3. Part 3 - Twin Delayed DDPG Implementation
    • 1. Beginning-en_US.srt (8.2 KB)
    • 1. Beginning.mp4 (20.0 MB)
    • 10. Implementation - Step 9-en_US.srt (5.2 KB)
    • 10. Implementation - Step 9.mp4 (32.7 MB)
    • 11. Implementation - Step 10-en_US.srt (5.5 KB)
    • 11. Implementation - Step 10.mp4 (28.1 MB)
    • 12. Implementation - Step 11-en_US.srt (10.3 KB)
    • 12. Implementation - Step 11.mp4 (63.1 MB)
    • 13. Implementation - Step 12-en_US.srt (5.8 KB)
    • 13. Implementation - Step 12.mp4 (33.6 MB)
    • 14. Implementation - Step 13-en_US.srt (7.9 KB)
    • 14. Implementation - Step 13.mp4 (47.2 MB)
    • 15. Implementation - Step 14-en_US.srt (9.8 KB)
    • 15. Implementation - Step 14.mp4 (51.2 MB)
    • 16. Implementation - Step 15-en_US.srt (19.3 KB)
    • 16. Implementation - Step 15.mp4 (179.6 MB)
    • 17. Implementation - Step 16-en_US.srt (12.5 KB)
    • 17. Implementation - Step 16.mp4 (78.1 MB)
    • 18. Implementation - Step 17-en_US.srt (8.1 KB)
    • 18. Implementation - Step 17.mp4 (79.1 MB)
    • 19. Implementation - Step 18-en_US.srt (20.4 KB)
    • 19. Implementation - Step 18.mp4 (156.3 MB)
    • 2. Implementation - Step 1-en_US.srt (20.6 KB)
    • 2. Implementation - Step 1.mp4 (76.2 MB)
    • 20. Implementation - Step 19-en_US.srt (16.8 KB)
    • 20. Implementation - Step 19.mp4 (98.7 MB)
    • 21. Implementation - Step 20-en_US.srt (7.9 KB)
    • 21. Implementation - Step 20.mp4 (66.7 MB)
    • 3. Implementation - Step 2-en_US.srt (19.9 KB)
    • 3. Implementation - Step 2.mp4 (202.2 MB)
    • 4. Implementation - Step 3-en_US.srt (17.5 KB)
    • 4. Implementation - Step 3.mp4 (136.0 MB)
    • 5. Implementation - Step 4-en_US.srt (18.7 KB)
    • 5. Implementation - Step 4.mp4 (194.8 MB)
    • 6. Implementation - Step 5-en_US.srt (14.9 KB)
    • 6. Implementation - Step 5.mp4 (55.5 MB)
    • 7. Implementation - Step 6-en_US.srt (13.8 KB)
    • 7. Implementation - Step 6.mp4 (106.2 MB)
    • 8. Implementation - Step 7-en_US.srt (6.4 KB)
    • 8. Implementation - Step 7.mp4 (28.7 MB)
    • 9. Implementation - Step 8-en_US.srt (10.1 KB)
    • 9. Implementation - Step 8.mp4 (83.9 MB)
    • The whole code folder of the course with all the implementations.html (0.7 KB)
    4. The Final Demo!
    • 1. Demo - Training-en_US.srt (27.9 KB)
    • 1. Demo - Training.mp4 (187.2 MB)
    • 2. Demo - Inference-en_US.srt (15.3 KB)
    • 2. Demo - Inference.mp4 (124.3 MB)
    5. Annex 1 - Artificial Neural Networks
    • 1. Plan of Attack-en_US.srt (3.9 KB)
    • 1. Plan of Attack.mp4 (6.9 MB)
    • 2. The Neuron-en_US.srt (25.8 KB)
    • 2. The Neuron.mp4 (63.8 MB)
    • 3. The Activation Function-en_US.srt (12.2 KB)
    • 3. The Activation Function.mp4 (24.4 MB)
    • 4. How do Neural Networks Work-en_US.srt (20.0 KB)
    • 4. How do Neural Networks Work.mp4 (50.3 MB)
    • 5. How do Neural Networks Learn-en_US.srt (19.2 KB)
    • 5. How do Neural Networks Learn.mp4 (66.9 MB)
    • 6. Gradient Descent-en_US.srt (14.3 KB)
    • 6. Gradient Descent.mp4 (38.6 MB)
    • 7. Stochastic Gradient Descent-en_US.srt (12.5 KB)
    • 7. Stochastic Gradient Descent.mp4 (35.0 MB)
    • 8. Backpropagation-en_US.srt (7.0 KB)
    • 8. Backpropagation.mp4 (32.8 MB)
    6. Annex 2 - Q-Learning
    • 1. Plan of Attack-en_US.srt (5.7 KB)
    • 1. Plan of Attack.mp4 (9.6 MB)
    • 10. Q-Learning Visualization-en_US.srt (21.2 KB)
    • 10. Q-Learning Visualization.mp4 (80.9 MB)
    • 2. What is Reinforcement Learning-en_US.srt (18.6 KB)
    • 2. What is Reinforcement Learning.mp4 (33.8 MB)
    • 3. The Bellman Equation-en_US.srt (31.5 KB)
    • 3. The Bellman Equation.mp4 (65.5 MB)
    • 4. The Plan-en_US.srt (3.7 KB)
    • 4. The Plan.mp4 (11.6 MB)
    • 5. Markov Decision Process-en_US.srt (27.2 KB)
    • 5. Markov Decision Process.mp4 (65.2 MB)
    • 6. Policy vs Plan-en_US.srt (22.5 KB)
    • 6. Policy vs Plan.mp4 (36.6 MB)
    • 7. Living Penalty-en_US.srt (14.2 KB)
    • 7. Living Penalty.mp4 (62.8 MB)
    • 8. Q-Learning Intuition-en_US.srt (22.5 KB)
    • 8. Q-Learning Intuition.mp4 (40.1 MB)
    • 9. Temporal Difference-en_US.srt (28.9 KB)
    • 9. Temporal Difference.mp4 (49.5 MB)
    7. Annex 3 - Deep Q-Learning
    • 1. Plan of Attack-en_US.srt (3.6 KB)

    • Description

      Deep Reinforcement Learning 2.0



      Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
      Language: English | Size: 3.90 GB | Duration: 9h 38m
      The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG
      What you'll learn
      Q-Learning
      Deep Q-Learning
      Policy Gradient
      Actor Critic
      Deep Deterministic Policy Gradient (DDPG)
      Twin-Delayed DDPG (TD3)
      The Foundation Techniques of Deep Reinforcement Learning
      How to implement a state of the art AI model that is over performing the most challenging virtual applications

      Description
      Welcome to Deep Reinforcement Learning 2.0!

      In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field).

      To approach this model the right way, we structured the course in three parts:



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Deep Reinforcement Learning 2.0

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Download torrent
3.9 GB
seeders:10
leechers:6
Deep Reinforcement Learning 2.0

Torrent hash: CD31EC0DEE7A953E2E0E5DC56D0C27138E37F078