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Evolutionary Deep Learning

Name: Evolutionary Deep Learning
Brand: Manning Publications
SKU: 39130484
Price: 70.63 EUR
Availability: InStock
Author: Lanham
ISBN: 9781617299520

Lanham

Micheal

Jazyk

Angličtina

Kniha Brožovaná

Libristo kód: 39130484

Nakladateľstvo Manning Publications, august 2023

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the p... Celý popis

Libristo kód: 39130484

171 b

70.63 €

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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.