Machine Learning: Beginner Reinforcement Learning in Python
How to teach a neural network to play a game using delayed gratification in 146 lines of Python code
Bullstrack
Machine Learning: Beginner Reinforcement Learning in Python udemy course
How to teach a neural network to play a game using delayed gratification in 146 lines of Python code
What you'll learn:
- Machine Learning
- Artificial Intelligence
- Neural Networks
- Reinforcement Learning
- Deep Q Learning
- OpenAI Gym
- Keras
- Tensorflow
- Bellman Equation
Requirements:
- Basic knowledge of Python
Description:
This course is designed for beginners to machine learning. Some of the most exciting advances in artificial intelligence have occurred by challenging neural networks to play games. I will introduce the concept of reinforcement learning, by teaching you to code a neural network in Python capable of delayed gratification.
We will use the NChain game provided by the Open AI institute. The computer gets a small reward if it goes backwards, but if it learns to make short term sacrifices by persistently pressing forwards it can earn a much larger reward. Using this example I will teach you Deep Q Learning - a revolutionary technique invented by Google DeepMind to teach neural networks to play chess, Go and Atari.
Who this course is for:
Course Details:
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2 hours on-demand video
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7 downloadable resources
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Full lifetime access
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Access on mobile and TV
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Certificate of completion
Machine Learning: Beginner Reinforcement Learning in Python udemy free download
How to teach a neural network to play a game using delayed gratification in 146 lines of Python code
Demo Link: https://www.udemy.com/course/machine-learning-beginner-reinforcement-learning-in-python/
