Evolutionary Deep Learning you own this product

Genetic algorithms and neural networks
Micheal Lanham
  • MEAP began December 2021
  • Publication in Early 2023 (estimated)
  • ISBN 9781617299520
  • 350 pages (estimated)
  • printed in black & white
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Look inside
Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment.

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 book

In Evolutionary Deep Learning you’ll master a toolbox of EC techniques that can be applied to any stage of the deep learning pipeline—from data collection, to hyperparameter tuning, and even optimizing network architecture. Hands-on examples demonstrate genetic algorithms and other EC approaches in action, and apply evolutionary deep learning to network topology, criterion loss and rewards, generative modeling, and reinforcement learning.

Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements.

about the reader

For data scientists who know Python.

about the author

Micheal Lanham is a proven software and tech innovator with over 20 years of experience. He has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development.

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