**Author**: Рамсундар Бхарат

**Publisher:**БХВ-Петербург

**ISBN:**5977540140

**Size**: 60.58 MB

**Format:**PDF, ePub, Mobi

**Category :**Computers

**Languages :**ru

**Pages :**256

**View:**5657

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# Tensorflow For Deep Learning PDF Download

**Author**: Рамсундар Бхарат

**Publisher:** БХВ-Петербург

**ISBN:** 5977540140

**Size**: 60.58 MB

**Format:** PDF, ePub, Mobi

**Category : **Computers

**Languages : **ru

**Pages : **256

**View:** 5657

## Tensorflow For Deep Learning Book Description:

Книга знакомит с основами программной библиотеки TensorFlow и принципами глубокого обучения, начиная с нулевого уровня. В книге рассмотрены базовые вычисления в библиотеке TensorFlow, простые обучающиеся системы и их построение, полносвязные глубокие сети, прототипы и превращение прототипов в высококачественные модели, сверхточные нейронные сети и обработка изображений, рекуррентные нейронные сети и наборы естественно-языковых данных, способы обучения с максимизацией подкрепления на примере известных игр, приемы тренинга глубоких сетей с помощью графических и тензорных процессоров.

# Deep Learning PDF Download

**Author**: Stellan Ohlsson

**Publisher:** Cambridge University Press

**ISBN:** 1139496751

**Size**: 54.80 MB

**Format:** PDF, ePub

**Category : **Psychology

**Languages : **en

**Pages : **

**View:** 773

## Deep Learning Book Description:

Although the ability to retain, process, and project prior experience onto future situations is indispensable, the human mind also possesses the ability to override experience and adapt to changing circumstances. Cognitive scientist Stellan Ohlsson analyzes three types of deep, non-monotonic cognitive change: creative insight, adaptation of cognitive skills by learning from errors, and conversion from one belief to another, incompatible belief. For each topic, Ohlsson summarizes past research, re-formulates the relevant research questions, and proposes information-processing mechanisms that answer those questions. The three theories are based on the principles of redistribution of activation, specialization of practical knowledge, and re-subsumption of declarative information. Ohlsson develops the implications of those mechanisms by scaling their effects with respect to time, complexity, and social interaction. The book ends with a unified theory of non-monotonic cognitive change that captures the abstract properties that the three types of change share.

# Deep Learning PDF Download

**Author**: Ian Goodfellow

**Publisher:** MIT Press

**ISBN:** 0262035618

**Size**: 72.21 MB

**Format:** PDF, Docs

**Category : **Computers

**Languages : **en

**Pages : **775

**View:** 5636

## Deep Learning Book Description:

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 With R For Beginners PDF Download

**Author**: Mark Hodnett

**Publisher:** Packt Publishing Ltd

**ISBN:** 1838647228

**Size**: 40.62 MB

**Format:** PDF, Docs

**Category : **Computers

**Languages : **en

**Pages : **612

**View:** 5239

## Deep Learning With R For Beginners Book Description:

Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the fundamentals of deep learning and neural networks Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing Implement effective deep learning systems in R with the help of end-to-end projects Book Description Deep learning finds practical applications in several domains, while R is the preferred language for designing and deploying deep learning models. This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you’ll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The book will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you’ll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R. By the end of this Learning Path, you’ll be well versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects. This Learning Path includes content from the following Packt products: R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark Hodnett R Deep Learning Projects by Yuxi (Hayden) Liu and Pablo Maldonado What you will learn Implement credit card fraud detection with autoencoders Train neural networks to perform handwritten digit recognition using MXNet Reconstruct images using variational autoencoders Explore the applications of autoencoder neural networks in clustering and dimensionality reduction Create natural language processing (NLP) models using Keras and TensorFlow in R Prevent models from overfitting the data to improve generalizability Build shallow neural network prediction models Who this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.

# Deep Learning With Structured Data PDF Download

**Author**: Mark Ryan

**Publisher:** Manning Publications

**ISBN:** 1617296724

**Size**: 64.32 MB

**Format:** PDF, Kindle

**Category : **Computers

**Languages : **en

**Pages : **273

**View:** 3633

## Deep Learning With Structured Data Book Description:

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. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

# R Deep Learning Essentials PDF Download

**Author**: Dr. Joshua F. Wiley

**Publisher:** Packt Publishing Ltd

**ISBN:** 1785284711

**Size**: 36.27 MB

**Format:** PDF, ePub

**Category : **Computers

**Languages : **en

**Pages : **170

**View:** 3281

## R Deep Learning Essentials Book Description:

Build automatic classification and prediction models using unsupervised learning About This Book Harness the ability to build algorithms for unsupervised data using deep learning concepts with R Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models Build models relating to neural networks, prediction and deep prediction Who This Book Is For This book caters to aspiring data scientists who are well versed with machine learning concepts with R and are looking to explore the deep learning paradigm using the packages available in R. You should have a fundamental understanding of the R language and be comfortable with statistical algorithms and machine learning techniques, but you do not need to be well versed with deep learning concepts. What You Will Learn Set up the R package H2O to train deep learning models Understand the core concepts behind deep learning models Use Autoencoders to identify anomalous data or outliers Predict or classify data automatically using deep neural networks Build generalizable models using regularization to avoid overfitting the training data In Detail Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning. This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples. After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models. Style and approach This book takes a practical approach to showing you the concepts of deep learning with the R programming language. We will start with setting up important deep learning packages available in R and then move towards building models related to neural network, prediction, and deep prediction - and all of this with the help of real-life examples.

# Deep Learning Applications And Intelligent Decision Making In Engineering PDF Download

**Author**: Senthilnathan, Karthikrajan

**Publisher:** IGI Global

**ISBN:** 1799821102

**Size**: 43.20 MB

**Format:** PDF, Kindle

**Category : **Technology & Engineering

**Languages : **en

**Pages : **332

**View:** 3107

## Deep Learning Applications And Intelligent Decision Making In Engineering Book Description:

Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process. Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.

# Facilitating Deep Learning PDF Download

**Author**: Julian Hermida

**Publisher:** CRC Press

**ISBN:** 1771880058

**Size**: 28.97 MB

**Format:** PDF, Kindle

**Category : **Business & Economics

**Languages : **en

**Pages : **370

**View:** 7334

## Facilitating Deep Learning Book Description:

Deep learning is a committed approach to learning. It is a process of constructing and interpreting new knowledge in light of prior cognitive structures and experiences, which can be applied in new, unfamiliar contexts. Deep learning produces learning that lasts a lifetime; and it results in better quality learning and profound understanding. In contrast, surface learning involves a dispassionate approach to learning. The surface learner is not concerned with understanding. Information acquired is usually lost after examinations; and there is no profound understanding or knowledge construction. Research studies show that most university and college students today take a surface approach to learning. The purpose of this book is to show readers how to create a learning environment that promotes deep learning in their classes. The book will do so by providing readers with the theoretical and pedagogical tools needed to: • Understand the notion of deep learning • Design and implement courses that encourage students to take a deep approach to learning • Design engaging and innovative teaching and learning activities that encourage students to use higher-order cognitive skills to construct knowledge and negotiate meaning • Implement assessment tools aimed at facilitating the deep learning process • Support international and other nontraditional students to construct learning deeply. The book begins with an examination of the big picture: the institutional constraints that hinder a culture of deep learning. From there, it deconstructs the concept of deep learning, and it examines every element of the deep learning process. It also discusses the factors that contribute to produce a deep learning environment. The rest of the chapters are about how to facilitate deep learning. The book examines every component of the teaching and learning system: goals, performances, and evaluation. It discusses strategies and methods that teachers can adopt to help students learn how to read and write in their disciplines in a deep way. The book also discusses the notion of inclusive deep learning environments which focus on engaging nontraditional students.

# Deep Learning Algorithms And Applications PDF Download

**Author**: Witold Pedrycz

**Publisher:** Springer Nature

**ISBN:** 3030317609

**Size**: 42.45 MB

**Format:** PDF, ePub, Docs

**Category : **Technology & Engineering

**Languages : **en

**Pages : **360

**View:** 921

## Deep Learning Algorithms And Applications Book Description:

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

# Autonomous Robotics And Deep Learning PDF Download

**Author**: Vishnu Nath

**Publisher:** Springer Science & Business Media

**ISBN:** 3319056034

**Size**: 70.55 MB

**Format:** PDF, Mobi

**Category : **Computers

**Languages : **en

**Pages : **66

**View:** 7539

## Autonomous Robotics And Deep Learning Book Description:

This Springer Brief examines the combination of computer vision techniques and machine learning algorithms necessary for humanoid robots to develop “true consciousness.” It illustrates the critical first step towards reaching “deep learning,” long considered the holy grail for machine learning scientists worldwide. Using the example of the iCub, a humanoid robot which learns to solve 3D mazes, the book explores the challenges to create a robot that can perceive its own surroundings. Rather than relying solely on human programming, the robot uses physical touch to develop a neural map of its environment and learns to change the environment for its own benefit. These techniques allow the iCub to accurately solve any maze, if a solution exists, within a few iterations. With clear analysis of the iCub experiments and its results, this Springer Brief is ideal for advanced level students, researchers and professionals focused on computer vision, AI and machine learning.