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Evolutionary Deep Learning: Genetic algorithms and neural networks
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
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. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science.
About the book
Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore.
What's inside
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.
Table of Contents
PART 1 - GETTING STARTED
1 Introducing evolutionary deep learning
2 Introducing evolutionary computation
3 Introducing genetic algorithms with DEAP
4 More evolutionary computation with DEAP
PART 2 - OPTIMIZING DEEP LEARNING
5 Automating hyperparameter optimization
6 Neuroevolution optimization
7 Evolutionary convolutional neural networks
PART 3 - ADVANCED APPLICATIONS
8 Evolving autoencoders
9 Generative deep learning and evolution
10 NeuroEvolution of Augmenting Topologies
11 Evolutionary learning with NEAT
12 Evolutionary machine learning and beyond
In Evolutionary Deep Learning you will learn how
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. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science.
About the book
Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore.
What's inside
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.
Table of Contents
PART 1 - GETTING STARTED
1 Introducing evolutionary deep learning
2 Introducing evolutionary computation
3 Introducing genetic algorithms with DEAP
4 More evolutionary computation with DEAP
PART 2 - OPTIMIZING DEEP LEARNING
5 Automating hyperparameter optimization
6 Neuroevolution optimization
7 Evolutionary convolutional neural networks
PART 3 - ADVANCED APPLICATIONS
8 Evolving autoencoders
9 Generative deep learning and evolution
10 NeuroEvolution of Augmenting Topologies
11 Evolutionary learning with NEAT
12 Evolutionary machine learning and beyond
- GenresArtificial Intelligence
360 pages, Paperback
Published July 18, 2023
22 people want to read
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Displaying 1 - 5 of 5 reviews
July 23, 2023
This book amalgamates Genetics Algorithms(GA) & Evolutionary Computation(EC) into the field of Deep Learning(DL). The author, Michael Lanham, has written some chapters in a comparative manner between the traditional DL approaches and evolutionary computation ones that facilitates easy grasping of concepts to learners.
Although some prior basic knowledge in Python, Data Science concepts and Deep Learning along with familiarity with Darwin's Evolutionary principle is required but overall, a must read for Data Scientist and developers looking to optimize Deep Learning models using bio or nature-inspired algorithms.
Although some prior basic knowledge in Python, Data Science concepts and Deep Learning along with familiarity with Darwin's Evolutionary principle is required but overall, a must read for Data Scientist and developers looking to optimize Deep Learning models using bio or nature-inspired algorithms.
July 27, 2023
Please don’t think you’ve got the right if you haven’t got through optimization. You would be able to undertake major achievements and truly impressive results, above the best, considering the usage of Evolutionary Computation as part of your daily basis project development. If you are willing to explore in this direction, this book will be very useful for you, even if you are not familiarized or have some background, Lanham clearly explain each concept from the very beginning, guiding the reader to full understanding for further implementation.
August 18, 2023
This book offers an insightful exploration of the potential of combining evolutionary algorithms and deep learning, two existing powerful subfields of artificial intelligence. The book is well-structured, providing both beginners and more experienced readers a valuable guide to understanding and implementing this emerging field.
July 29, 2023
This was a fun book to go over! The ideas of evolutionary algorithms are explained in a clear way, and the coding examples are relevant and interesting.
Highly recommended to practitioners who work on optimization problems and deep learning.
Highly recommended to practitioners who work on optimization problems and deep learning.
July 19, 2023
This books makes AI/ML learning easy with lot's of real time examples. Throughout the book it gradually approached the optimized way of solving the problems.
Displaying 1 - 5 of 5 reviews