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
Created by Milo Spencer-Harper | 1.5 hours on-demand video course
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.
What you’ll learn
- Machine Learning
- Artificial Intelligence
- Neural Networks
- Reinforcement Learning
- Deep Q Learning
- OpenAI Gym
- Keras
- Tensorflow
- Bellman Equation
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