Deep Learning

Written By Ian Goodfellow
Deep Learning
  • Publsiher : MIT Press
  • Release : 18 November 2016
  • ISBN : 0262035618
  • Pages : 775 pages
  • Rating : 3.5/5 from 5 reviews
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Read or download book entitled Deep Learning written by Ian Goodfellow which was release on 18 November 2016, this book published by MIT Press. Available in PDF, EPUB and Kindle Format. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Deep Learning
  • Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
  • Publisher : MIT Press
  • Release Date : 2016-11-18
  • Total pages : 775
  • ISBN : 0262035618
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Summary : An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO ...

Deep Learning

Deep Learning
  • Author : Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release Date : 2020-06-22
  • Total pages : 161
  • ISBN : 0262035618
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Summary : This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced ...

Deep Learning

Deep Learning
  • Author : Frank Millstein
  • Publisher : Frank Millstein
  • Release Date : 2020-08-14
  • Total pages : 266
  • ISBN : 0262035618
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Summary : Deep Learning - 2 BOOK BUNDLE!! Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and ...

Deep Learning with Structured Data

Deep Learning with Structured Data
  • Author : Mark Ryan
  • Publisher : Manning Publications
  • Release Date : 2020-12-29
  • Total pages : 273
  • ISBN : 0262035618
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Summary : Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. ...

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Author : Eli Stevens,Luca Antiga,Thomas Viehmann
  • Publisher : Manning Publications
  • Release Date : 2020-08-04
  • Total pages : 520
  • ISBN : 0262035618
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Summary : Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with ...

Practical Deep Learning for Cloud Mobile and Edge

Practical Deep Learning for Cloud  Mobile  and Edge
  • Author : Anirudh Koul,Siddha Ganju,Meher Kasam
  • Publisher : O'Reilly Media
  • Release Date : 2019-10-14
  • Total pages : 620
  • ISBN : 0262035618
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Summary : Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning ...

Deep Learning for Coders with fastai and PyTorch

Deep Learning for Coders with fastai and PyTorch
  • Author : Jeremy Howard,Sylvain Gugger
  • Publisher : O'Reilly Media
  • Release Date : 2020-06-29
  • Total pages : 624
  • ISBN : 0262035618
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Summary : Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library ...

Deep Learning for the Life Sciences

Deep Learning for the Life Sciences
  • Author : Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
  • Publisher : O'Reilly Media
  • Release Date : 2019-04-10
  • Total pages : 238
  • ISBN : 0262035618
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Summary : Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing ...

Handbook of Research on the Impact of Deep Learning and IoT on Multi Industry Applications

Handbook of Research on the Impact of Deep Learning and IoT on Multi Industry Applications
  • Author : Roshani Raut,Albena Dimitrova Mihovska
  • Publisher : Engineering Science Reference
  • Release Date : 2021-01-29
  • Total pages : 400
  • ISBN : 0262035618
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Summary : "This book provides insights on how deep learning, together with IOT, will impact various sectors such as healthcare, agriculture, cyber security, and social media analysis applications offering solutions to various real-world problems using these methods from various researchers' point of views"--...

Deep Learning

Deep Learning
  • Author : Josh Patterson,Adam Gibson
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2017-07-28
  • Total pages : 532
  • ISBN : 0262035618
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Summary : How can machine learning--especially deep neural networks--make a real difference in your organization? This hands-on guide not only provides practical information, but helps you get started building efficient deep learning networks. The authors provide the fundamentals of deep learning--tuning, parallelization, vectorization, and building pipelines--that are valid for any library before ...

The Deep Learning Workshop

The Deep Learning Workshop
  • Author : Mirza Rahim Baig,Thomas V. Joseph,Nipun Sadvilkar,Mohan Kumar Silaparasetty,Anthony So
  • Publisher : Packt Publishing Ltd
  • Release Date : 2020-07-31
  • Total pages : 474
  • ISBN : 0262035618
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Summary : Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are ...

Deep Learning for Image Processing Applications

Deep Learning for Image Processing Applications
  • Author : D.J. Hemanth,V. Vieira Estrela
  • Publisher : IOS Press
  • Release Date : 2017-12
  • Total pages : 284
  • ISBN : 0262035618
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Summary : Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, ‘Deep Learning for Image Processing Applications’, is to ...

Deep Learning with Keras

Deep Learning with Keras
  • Author : Frank Millstein
  • Publisher : Frank Millstein
  • Release Date : 2020-07-07
  • Total pages : 148
  • ISBN : 0262035618
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Summary : Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will ...

Deep Learning

Deep Learning
  • Author : Julius Porter
  • Publisher : Unknown
  • Release Date : 2016-01-01
  • Total pages : 76
  • ISBN : 0262035618
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Summary : Deep Learning is gaining more and more popularity due to its success in various applications like Natural Language Processing (NLP), Image recognition and other Machine Learning (ML) paradigms. There are three conventional approaches that formed the basis for deep learning, Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs) and Stacked ...

Functional Brain Network Analysis Based on Unsupervised Deep Learning

Functional Brain Network Analysis Based on Unsupervised Deep Learning
  • Author : Qinglin Dong
  • Publisher : Unknown
  • Release Date : 2019
  • Total pages : 198
  • ISBN : 0262035618
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Summary : In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain network (FBN), such as general linear models (GLM), independent component analysis (ICA) and sparse dictionary learning (SDL). Recently, deep learning has attracted much attention in the fields of machine ...