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4 |
|a Q325.73
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| 100 |
1 |
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|a Lanham, Micheal
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| 245 |
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|a Evolutionary deep learning
|c Michael Lanham
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| 260 |
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|a [Place of publication not identified]
|b Manning Publications
|c 2023
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| 300 |
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|a 1 audio file
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| 653 |
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|a Apprentissage profond
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Deep learning (Machine learning) / http://id.loc.gov/authorities/subjects/sh2021006947
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| 653 |
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|a Apprentissage automatique
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781617299520AU/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3/1
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| 520 |
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|a 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.
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| 520 |
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|a What's Inside Solve complex design and analysis problems with evolutionary computation Tune deep learning hyperparameters 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 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. Quotes Use biology-inspired optimization methods to make quick work of machine learning model training and hyperparameter selection. - Dr. Erik Sapper, Cal Poly-San Luis Obispo Makes learning evolutionary practices with neural networks easy. - Ninoslav Čerkez, Rimac Technology Data science meets optimization! Includes wonderful scenarios where optimization is applied to improve AI, ML, deep learning, and so on. We're living in a transdisciplinary age! - Ricardo Di Pasquale, Accenture
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| 520 |
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|a In this one-of-a-kind guide, you'll discover tools for optimizing everything from data collection to your network architecture. 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.
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| 520 |
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|a 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.
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