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Evolutionary Deep Learning

Evolutionary Deep Learning


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About the Book

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.

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.

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

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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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Product Details
  • ISBN-13: 9781617299520
  • Publisher: Manning Publications
  • Publisher Imprint: Manning Publications
  • Height: 235 mm
  • No of Pages: 350
  • Spine Width: 22 mm
  • Weight: 594 gr
  • ISBN-10: 1617299529
  • Publisher Date: 27 Dec 2022
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: Genetic Algorithms and Neural Networks
  • Width: 187 mm


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