Description

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 reader For data scientists who know Python.

Evolutionary Deep Learning

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Paperback / softback by Micheal Lanham

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Short Description:

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep... Read more

    Publisher: Manning Publications
    Publication Date: 06/07/2023
    ISBN13: 9781617299520, 978-1617299520
    ISBN10: 1617299529

    Number of Pages: 350

    Non Fiction , Computing

    Description

    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 reader For data scientists who know Python.

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