How to teach a neural network to play a game using delayed gratification in 146 lines of Python code
4.6 | (143 ratings) | 454 students | Author: Milo Spencer-Harper
Course Duration:
5 sections • 24 lectures • 1h 44m total length
4.6 | (143 ratings) | 454 students | Author: Milo Spencer-Harper
Course Duration:
5 sections • 24 lectures • 1h 44m total length
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:
- Anyone interested in machine learning
