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Neural networks and deep learning: a textbook

By: Aggarwal, Charu C.
Material type: materialTypeLabelBookPublisher: New York Springer 2018Description: xxi, 497 p.ISBN: 9783319944623.Subject(s): Machine learning | Neural networks | Computer science | Artificial intelligence | Microprocessors | Computer science | ComputersDDC classification: 006.32 Summary: This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. https://www.springer.com/in/book/9783319944623
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Item type Current location Item location Collection Call number Status Date due Barcode
Books Vikram Sarabhai Library
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Slot 105 (0 Floor, West Wing) Non-fiction 006.32 A4N3 (Browse shelf) Checked out 16/12/2019 199800

Table of contents

An Introduction to Neural Networks

Machine Learning with Shallow Neural Networks

Training Deep Neural Networks

Teaching Deep Learners to Generalize

Radial Basis Function Networks

Restricted Boltzmann Machines

Recurrent Neural Networks

Convolutional Neural Networks

Deep Reinforcement Learning

Advanced Topics in Deep Learning

This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems.

https://www.springer.com/in/book/9783319944623

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