Deep Learning: Research and Applications 9783110670790

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Автор: Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy
Название: Deep Learning: Research and Applications
ISBN: 9783110670790
Издательство: Walter de Gruyter
Классификация:

Робототехника

Алгоритмы и структуры данных

Компьютерные сети и коммуникации

Искусственный интеллект

Машинное обучение

Нейронные сети и нечеткие системы

Распознавание образов

Машинное зрение

ISBN-10: 3110670798
Обложка/Формат: Hardback
Страницы: 180
Вес: 0.47 кг.
Дата издания: 22.06.2020
Серия: Computing & IT
Язык: English
Размер: 244 x 170 x 11
Читательская аудитория: Professional and scholarly
Ключевые слова: Algorithms & data structures,Artificial intelligence,Computer networking & communications, COMPUTERS / Computer Vision & Pattern Recognition,COMPUTERS / Information Technology,COMPUTERS / Intelligence (AI) & Semantics,COMPUTERS / Neural Networks,COMPUTERS
Поставляется из: Германии
Описание: This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter. Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. The unique features of this book include: • tutorials on deep learning framework with focus on tensor flow, keras etc. • video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. • a score of worked out examples on real life applications. • illustrative diagrams • coding examples

Deep Learning: Research and Applications, Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy