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Neural Networks and Deep Learning: A Textbook

By: Material type: TextTextPublication details: Cham : Springer, 2023Edition: Second editionDescription: xxiv, 529 pISBN:
  • 9783031296444
  • 9783031296420
  • 3031296443
Subject(s): DDC classification:
  • 006.32 AGG
Contents:
An Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.
Summary: Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.
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Item type Current library Collection Call number Status Date due Barcode Item holds
Reference Books Reference Books Main Library Reference Reference 006.32 AGG (Browse shelf(Opens below)) Available 016734
Total holds: 0

Includes index

An Introduction to Neural Networks.- The Backpropagation Algorithm.- Machine Learning with Shallow Neural Networks.- Deep Learning: Principles and Training Algorithms.- Teaching a Deep Neural Network to Generalize.- Radial Basis Function Networks.- Restricted Boltzmann Machines.- Recurrent Neural Networks.- Convolutional Neural Networks.- Graph Neural Networks.- Deep Reinforcement Learning.- Advanced Topics in Deep Learning.

Chapters 6 and 7 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 8, 9, and 10 discuss recurrent neural networks, convolutional neural networks, and graph neural networks.

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