Deep Learning: A Practitioner`s Approach, Gibson Adam, Patterson Josh

Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron
Название: Deep Learning
ISBN: 0262035618 ISBN-13(EAN): 9780262035613
Издательство: MIT Press
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Цена: 13543.00 р.
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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: Trask Andrew
Название: Grokking Deep Learning
ISBN: 1617293709 ISBN-13(EAN): 9781617293702
Издательство: Pearson Education
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Цена: 7390.00 р.
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Описание: Key Features:* Build neural networks that can see and understand images* Build an A.I. that will learn to defeat you in a classic Atari game* Hands-on Learning Written for readers with high school-level math and intermediateprogramming skills. Experience with Calculus is helpful but notrequired.

Автор: Boschetti Alberto, Massaron Luca, Thakur Abhishek
Название: Tensorflow Deep Learning Projects
ISBN: 1788398068 ISBN-13(EAN): 9781788398060
Издательство: Неизвестно
Цена: 8091.00 р.
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Описание: This book is your guide to master deep learning with TensorFlow, with the help of 10 real-world projects. You will train high-performance models in TensorFlow to generate captions for images automatically, predict stocks` performance, create intelligent chatbots, perform large-scale text classification, develop recommendation systems, and more.

Автор: Charniak Eugene
Название: Introduction to Deep Learning
ISBN: 0262039516 ISBN-13(EAN): 9780262039512
Издательство: MIT Press
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Цена: 5925.00 р.
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Описание:

A project-based guide to the basics of deep learning.

This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. "I find I learn computer science material best by sitting down and writing programs," the author writes, and the book reflects this approach.

Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.

Автор: Hodnett Mark
Название: R Deep Learning Essentials: Second Edition
ISBN: 178899289X ISBN-13(EAN): 9781788992893
Издательство: Неизвестно
Цена: 8091.00 р.
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Описание: This book demonstrates how to use deep Learning in R for machine learning, image classification, and natural language processing. It covers topics such as convolutional networks, recurrent neural networks, transfer learning and deep learning in the cloud. By the end of this book, you will be able to apply deep learning to real-world projects.

Автор: Zaccone Giancarlo, Karim MD Rezaul
Название: Deep Learning with Tensorflow - Second Edition
ISBN: 1788831101 ISBN-13(EAN): 9781788831109
Издательство: Неизвестно
Цена: 8091.00 р.
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Описание: Compliant with TensorFlow 1.7, this book introduces the core concepts of deep learning. Get implementation and research details on cutting-edge architectures and apply advanced concepts to your own projects. Develop your knowledge of deep neural networks through hands-on model building and examples of real-world data collection.

Автор: Zhou, Kevin
Название: Deep Learning for Medical Image Analysis
ISBN: 0128104082 ISBN-13(EAN): 9780128104088
Издательство: Elsevier Science
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Цена: 16505.00 р.
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Описание:

Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas.

Автор: Gulli Antonio, Kapoor Amita
Название: Tensorflow 1.X Deep Learning Cookbook
ISBN: 1788293592 ISBN-13(EAN): 9781788293594
Издательство: Неизвестно
Цена: 9010.00 р.
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Описание: Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book - Skill up and implement tricky neural networks using Google's TensorFlow 1.x - An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. - Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn - Install TensorFlow and use it for CPU and GPU operations - Implement DNNs and apply them to solve different AI-driven problems. - Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. - Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. - Use different regression techniques for prediction and classification problems - Build single and multilayer perceptrons in TensorFlow - Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. - Learn how restricted Boltzmann Machines can be used to recommend movies. - Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. - Master the different reinforcement learning methods to implement game playing agents. - GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.

Автор: Karthik S., Paul Anand, Karthikeyan N.
Название: Deep Learning Innovations and Their Convergence with Big Data
ISBN: 1522530150 ISBN-13(EAN): 9781522530152
Издательство: Mare Nostrum (Eurospan)
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Цена: 29938.00 р.
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Описание: The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection

Автор: Hope Tom, Resheff Yehezkel S., Lieder Itay
Название: Learning Tensorflow: A Guide to Building Deep Learning Systems
ISBN: 1491978511 ISBN-13(EAN): 9781491978511
Издательство: Wiley
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Цена: 7602.00 р.
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Описание: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.

Название: Deep learning in biometrics
ISBN: 1138578231 ISBN-13(EAN): 9781138578234
Издательство: Taylor&Francis
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Цена: 22202.00 р.
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Описание: This book will cover all the topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoenders. The focus will be on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints.