Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 13543.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
Автор: Chollet Francois Название: Deep Learning with Python, Second Edition ISBN: 1617296864 ISBN-13(EAN): 9781617296864 Издательство: Неизвестно Рейтинг: Цена: 11033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: "Chollet is a master of pedagogy and explains complex concepts with minimal fuss, cutting through the math with practical Python code. He is also an experienced ML researcher and his insights on various model architectures or training tips are a joy to read." - Martin Gцrner, GooglePrinted in full color! Unlock the groundbreaking advances of deep learning with this extensively revised new edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world. In Deep Learning with Python, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Timeseries forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full color second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You'll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is quickly becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach--even if you have no background in mathematics or data science. This book shows you how to get started. About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this revised and expanded new edition, Keras creator Franзois Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you'll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You'll quickly pick up the skills you need to start developing deep-learning applications. What's inside Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Full color printing throughout About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author Franзois Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions
Автор: Zhang, Aston (amazon Web Services) Lipton, Zachary Название: Dive into deep learning ISBN: 1009389432 ISBN-13(EAN): 9781009389433 Издательство: Cambridge Academ Рейтинг: Цена: 3958.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse ?elds as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for ?tting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required—every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.
Описание: CONVERGENCE OF DEEP LEARNING IN CYBER-IOT SYSTEMS AND SECURITY In-depth analysis of Deep Learning-based cyber-IoT systems and security which will be the industry leader for the next ten years. The main goal of this book is to bring to the fore unconventional cryptographic methods to provide cyber security, including cyber-physical system security and IoT security through deep learning techniques and analytics with the study of all these systems. This book provides innovative solutions and implementation of deep learning-based models in cyber-IoT systems, as well as the exposed security issues in these systems.
The 20 chapters are organized into four parts. Part I gives the various approaches that have evolved from machine learning to deep learning. Part II presents many innovative solutions, algorithms, models, and implementations based on deep learning.
Part III covers security and safety aspects with deep learning. Part IV details cyber-physical systems as well as a discussion on the security and threats in cyber-physical systems with probable solutions. Audience Researchers and industry engineers in computer science, information technology, electronics and communication, cybersecurity and cryptography.
Описание: The text emphasizes the importance of innovation and improving the profitability of manufacturing plants using smart technologies such as artificial intelligence, deep learning, and the internet of things. It further discusses applications of smart technologies in diverse sectors such as production, manufacturing, transport, and healthcare.
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
Автор: Mitsuhiro Toriumi Название: Geochemical Mechanics and Deep Neural Network Modeling ISBN: 9811936587 ISBN-13(EAN): 9789811936586 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The recent understandings about global earth mechanics are widely based on huge amounts of monitoring data accumulated using global networks of precise seismic stations, satellite monitoring of gravity, very large baseline interferometry, and the Global Positioning System. New discoveries in materials sciences of rocks and minerals and of rock deformation with fluid water in the earth also provide essential information. This book presents recent work on natural geometry, spatial and temporal distribution patterns of various cracks sealed by minerals, and time scales of their crack sealing in the plate boundary. Furthermore, the book includes a challenging investigation of stochastic earthquake prediction testing by means of the updated deep machine learning of a convolutional neural network with multi-labeling of large earthquakes and of the generative autoencoder modeling of global correlated seismicity. Their manifestation in this book contributes to the development of human society resilient from natural hazards. Presented here are (1) mechanics of natural crack sealing and fluid flow in the plate boundary regions, (2) large-scale permeable convection of the plate boundary, (3) the rapid process of massive extrusion of plate boundary rocks, (4) synchronous satellite gravity and global correlated seismicity, (5) Gaussian network dynamics of global correlated seismicity, and (6) prediction testing of plate boundary earthquakes by machine learning and generative autoencoders.
Описание: This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.
Автор: Edited By Roshani Raut, Pranav D Pathak, Sachin R Название: Generative Adversarial Networks and Deep Learning Theory and Applications ISBN: 1032068108 ISBN-13(EAN): 9781032068107 Издательство: Taylor&Francis Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications.
Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc. Features:Presents a comprehensive guide on how to use GAN for images and videos.
Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GANHighlights the inclusion of gaming effects using deep learning methodsExamines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutionsThe book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum
Описание: This book provides insights into deep learning techniques that impact the implementation strategies toward achieving the Sustainable Development Goals (SDGs) laid down by the United Nations for its 2030 agenda, elaborating on the promises, limits, and the new challenges. It also covers the challenges, hurdles, and opportunities in various applications of deep learning for the SDGs. A comprehensive survey on the major applications and research, based on deep learning techniques focused on SDGs through speech and image processing, IoT, security, AR-VR, formal methods, and blockchain, is a feature of this book. In particular, there is a need to extend research into deep learning and its broader application to many sectors and to assess its impact on achieving the SDGs. The chapters in this book help in finding the use of deep learning across all sections of SDGs. The rapid development of deep learning needs to be supported by the organizational insight and oversight necessary for AI-based technologies in general; hence, this book presents and discusses the implications of how deep learning enables the delivery agenda for sustainable development.
Автор: Ashish Kumar Название: Visual Object Tracking using Deep Learning ISBN: 1032490535 ISBN-13(EAN): 9781032490533 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Kolodiazhnyi, Kirill Название: Hands-on machine learning with c++ ISBN: 1789955335 ISBN-13(EAN): 9781789955330 Издательство: Неизвестно Рейтинг: Цена: 11033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will help you explore how to implement different well-known machine learning algorithms with various C++ frameworks and libraries. You will cover basic to advanced machine learning concepts with practical and easy to follow examples. By the end of the book, you will be able to build various machine learning models with ease.
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