Description
Reinforcement Learning beginner to master – AI in Python, the reinforcement learning course from beginner to advanced has been published by Udemy Academy. This is the most complete reinforcement learning course in Eudemy, and in it you will learn the principles of reinforcement learning, one of the three paradigms of modern artificial intelligence. From scratch, you will implement adaptive algorithms that solve control tasks based on experience. You will also learn to combine these algorithms with deep learning techniques and neural networks, a branch of deep reinforcement learning. It also prepares you for the next courses of this series, where we will continue to look at different branches of reinforcement learning and take a look at some of the more advanced algorithms available. This course focuses on developing practical skills; Therefore, after learning the most important concepts of each group of methods, we will implement one or more of their algorithms from the beginning in jupyter notebooks.
This course is divided into three parts and covers the following topics:
First part (Tabular methods):
- Markov decision process
- Dynamic programming
- Time difference methods (SARSA, Q-Learning)
- N-step bootstrapping
Section 2 (continuous state spaces):
- Summary of the situation
- Coding Tile
The third part (deep reinforcement learning):
- Deep SARS
- Deep Q-Learning
- strengthen
- Advantage Actor-Critic / A2C (Advantage Actor-Critic / A2C method)
What you will learn
- Understand the reinforcement learning model and the operation that is best suited to solve them.
- Understand different ways to solve an operation using reinforcement learning and choose the most appropriate one.
- Basically understand the learning process of any algorithm
- Understanding and implementing new algorithms from research articles
- Understanding the process of solving a diagnostic operation using reinforcement learning
- Full implementation of reinforcement learning algorithms from scratch
- Debugging and development of presented algorithms
Who is this course suitable for?
- Developers who want to get a job in machine learning
- Data scientists and ML professionals are looking to expand their knowledge base.
- Researchers looking to increase their practical coding skills.
Details of Reinforcement Learning course beginner to master – AI in Python
- Publisher: Yudmi
- teacher: Escape Velocity Labs
- English language
- Education level: all levels
- Number of courses: 131
- Training duration: 10 hours and 46 minutes
Head of the course chapters on 2022-12
Course prerequisites
- Be comfortable programming in Python
- Know basic linear algebra and calculus (matrices, vectors, determinants, derivatives, etc.)
- Know basic statistics and probability theory (mean, variance, normal distribution, etc.)
Images of Reinforcement Learning beginner to master – AI in Python
Sample video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
Version 2022/10 compared to 2022/6 has decreased by 16 minutes and increased by 9 lessons.
download link
Password file(s): www.downloadly.ir
Size
3 GB
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