Advanced Reinforcement Learning in Python: cutting-edge DQNs is an advanced reinforcement learning training course in Python published by Udemy Academy. During this training course, you will get to know the process of developing and building artificial intelligence-based assistants that use different deep learning and reinforcement learning techniques.
There are various algorithms in the field of deep reinforcement learning. During this training course, you will learn about the implementation process of the most advanced and important algorithms with the PyTorch framework and the PyTorch lighting tool. Implementing a set of adaptive algorithms that are able to solve a set of controlled tasks with a specific technical structure based on their previous experiences is one of the most important skills taught in this course.
In the final part of this training course, you will combine all the skills learned and the taught materials and use them in the development of an assistant based on artificial intelligence. This assistant is fully adaptive and can use artificial neural networks and many deep learning methods defined for it to make decisions in different situations and act based on the decisions made.
What you will learn in the Advanced Reinforcement Learning in Python: cutting-edge DQNs course:
- Advanced reinforcement learning
- PyTorch framework
- Hyperparameter tuning with Optional
- Reinforcement learning with raw image data
- Advanced and applied reinforcement learning algorithms
- Building artificial intelligence with the ability to make decisions in different situations
- Getting to know the learning process for each of the algorithms
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Instructor: Escape Velocity Labs
Training level: Introductory to advanced
Number of lessons: 102
Duration of training: 8 hours and 26 minutes
Advanced Reinforcement Learning in Python Course headings
Advanced Reinforcement Learning in Python course prerequisites: cutting-edge DQNs
Be comfortable programming in Python
Completing our course “Reinforcement Learning beginner to master” or being familiar with the basics of Reinforcement Learning (or watching the leveling sections included in this course).
Know basic statistics (mean, variance, normal distribution)
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