Evolutionary Deep Learning: Genetic Algorithms and Neural Networks

In Evolutionary Deep Learning you will learn how to:

• Solve complex design and analysis problems with evolutionary computation
• Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization
• Use unsupervised learning with a deep learning autoencoder to regenerate sample data
• Understand the basics of reinforcement learning and the Q Learning equation
• Apply Q Learning to deep learning to produce deep reinforcement learning
• Optimize the loss function and network architecture of unsupervised autoencoders
• Make an evolutionary agent that can play an OpenAI Gym game

Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning.

about the technology

Evolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data.

about the reader

For data scientists who know Python.