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Deep Learning: Research and Applications

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Description: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 examplesWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Deep Learning: Research and Applications. To get started finding Deep Learning: Research and Applications, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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ISBN: 3110670798

Description: 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 examplesWe have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Deep Learning: Research and Applications. To get started finding Deep Learning: Research and Applications, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Deep Learning: Research and Applications

Deep Learning: Research and Applications

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