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6/18/2020
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Deep Reinforcement Learning 2.0 is a training package on the subject of deep reinforcement learning. Deep reinforcement learning Uses the principles of deep learning and reinforcement learning to build efficient algorithms for robotics, computer games, natural language processing, computer vision, education, transportation, accounting, and healthcare. Implementing a deep learning architecture with enhanced learning algorithms is capable of solving previously unresolved problems. In the Deep Reinforcement Learning 2.0 course, a new ultra-intelligent AI model called Twin-Delayed DDPG is implemented, which combines artistic techniques in AI, including Double Deep Q-Learning, Policy Gradient and Actor Critic. Fully trained. This model is so powerful that you can use it to solve the most challenging AI programs. Background The beginning of learning in this course is knowing the basics of mathematics and some programming knowledge.
Q-Learning
Deep Q-Learning
Policy gradient
Actor Critic
Deep definite policy gradient
Twin-Delayed DDPG
Basic techniques of deep reinforcement learning
How to implement a mode of artificial intelligence model that has the ability to perform the most challenging virtual applications
Publisher: Udemy
Instructor: Hadelin de Ponteves, Kirill Eremenko
Elementary level
Duration: 09:39:02
Number of lessons: 62 lessons
English language
62 lectures 09:39:02
Part 1 – Fundamentals 10 lectures 53:45
Part 2 – Twin Delayed DDPG Theory 4 lectures 50:39
Part 3 – Twin Delayed DDPG Implementation 22 lectures 03:09:51
The Final Demo! 2 lectures 27:57
Annex 1 – Artificial Neural Networks 8 lectures 01:17:37
Annex 2 – Q-Learning 10 lectures 02:02:59
Annex 3 – Deep Q-Learning 5 lectures 55:46
Bonus Lectures 1 lecture 00:27
Some maths basics like knowing what is a differentiation or a gradient A bit of programming knowledge (classes and objects)
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
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