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 t...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Shelter Island
Manning
[2023]
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| Schlagworte: | |
| Zusammenfassung: | 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 computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q Learning equationApply Q Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake 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 |
| Beschreibung: | xxii, 336 Seiten Illustrationen, Diagramme |
| ISBN: | 9781617299520 |
Internformat
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| 520 | 3 | |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. In Evolutionary Deep Learning you will learn how to: Solve complex design and analysis problems with evolutionary computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q Learning equationApply Q Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake 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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Datensatz im Suchindex
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| institution | BVB |
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| spelling | Lanham, Micheal Verfasser (DE-588)1195226217 aut Evolutionary deep learning genetic algorithms and neural networks Micheal Lanham Shelter Island Manning [2023] xxii, 336 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier 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 computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q Learning equationApply Q Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake 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 Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf COM094000 COMPUTERS / Neural Networks Machine learning Maschinelles Lernen Maschinelles Lernen (DE-588)4193754-5 s Deep learning (DE-588)1135597375 s Neuronales Netz (DE-588)4226127-2 s DE-604 |
| spellingShingle | Lanham, Micheal Evolutionary deep learning genetic algorithms and neural networks Neuronales Netz (DE-588)4226127-2 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)1135597375 (DE-588)4193754-5 |
| title | Evolutionary deep learning genetic algorithms and neural networks |
| title_auth | Evolutionary deep learning genetic algorithms and neural networks |
| title_exact_search | Evolutionary deep learning genetic algorithms and neural networks |
| title_exact_search_txtP | Evolutionary deep learning genetic algorithms and neural networks |
| title_full | Evolutionary deep learning genetic algorithms and neural networks Micheal Lanham |
| title_fullStr | Evolutionary deep learning genetic algorithms and neural networks Micheal Lanham |
| title_full_unstemmed | Evolutionary deep learning genetic algorithms and neural networks Micheal Lanham |
| title_short | Evolutionary deep learning |
| title_sort | evolutionary deep learning genetic algorithms and neural networks |
| title_sub | genetic algorithms and neural networks |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Neuronales Netz Deep learning Maschinelles Lernen |
| work_keys_str_mv | AT lanhammicheal evolutionarydeeplearninggeneticalgorithmsandneuralnetworks |