Evolutionary Deep Learning: Genetic algorithms and neural networks by Micheal Lanham.
Today Michael is a Lead AI Developer at Brilliant Harvest Inc. and also a Technical author at Manning Publications. Previously he worked at Symend as a Principle AI Engineer and Manager of ML Engineering.
This books expands upon his knowledge in deep learning, evolutionary computation, and genetic algorithms. This was a very enlightening read and will be very interesting for those interested in a comprehensive understanding of deep learning today.
The book is certainly written for data scientists using Python. This offers a very deep perspective on the combination of evolutionary principles with deep learning.
This results in enhanced model performance with the ability to solve complex problems with machine learning.
Michael is able to address multiple themes related to the link with computation and deep learning techniques. The book is structured in three main parts:
- Getting Started:
Introduces evolutionary deep learning, evolutionary computation, and genetic algorithms using DEAP (Distributed Evolutionary Algorithms in Python). - Optimizing Deep Learning:
Covers automated hyperparameter optimization, neuroevolution optimization, and evolutionary convolutional neural networks. - Advanced Applications:
Explores evolving autoencoders, generative deep learning with evolution, NeuroEvolution of Augmenting Topologies (NEAT), and future directions in evolutionary machine learning..