Deep Learning Theory And Applications

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Deep Learning: Fundamentals, Theory and Applications

Kaizhu Huang,Amir Hussain,Qiu-Feng Wang,Rui Zhang
Deep Learning Fundamentals Theory and Applications Book PDF

Author : Kaizhu Huang,Amir Hussain,Qiu-Feng Wang,Rui Zhang
Publisher : Springer
Page : 163 pages
File Size : 55,6 Mb
Release : 2019-02-15
Category : Medical
ISBN : 9783030060732

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Deep Learning: Fundamentals, Theory and Applications by Kaizhu Huang,Amir Hussain,Qiu-Feng Wang,Rui Zhang Pdf

The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Machine Learning: From Theory to Applications

Stephen J. Hanson,Werner Remmele,Ronald L. Rivest
Machine Learning From Theory to Applications Book PDF

Author : Stephen J. Hanson,Werner Remmele,Ronald L. Rivest
Publisher : Springer Science & Business Media
Page : 292 pages
File Size : 51,9 Mb
Release : 1993-03-30
Category : Computers
ISBN : 3540564837

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Machine Learning: From Theory to Applications by Stephen J. Hanson,Werner Remmele,Ronald L. Rivest Pdf

This volume includes some of the key research papers in the area of machine learning produced at MIT and Siemens during a three-year joint research effort. It includes papers on many different styles of machine learning, organized into three parts. Part I, theory, includes three papers on theoretical aspects of machine learning. The first two use the theory of computational complexity to derive some fundamental limits on what isefficiently learnable. The third provides an efficient algorithm for identifying finite automata. Part II, artificial intelligence and symbolic learning methods, includes five papers giving an overview of the state of the art and future developments in the field of machine learning, a subfield of artificial intelligence dealing with automated knowledge acquisition and knowledge revision. Part III, neural and collective computation, includes five papers sampling the theoretical diversity and trends in the vigorous new research field of neural networks: massively parallel symbolic induction, task decomposition through competition, phoneme discrimination, behavior-based learning, and self-repairing neural networks.

The Principles of Deep Learning Theory

Daniel A. Roberts,Sho Yaida,Boris Hanin
The Principles of Deep Learning Theory Book PDF

Author : Daniel A. Roberts,Sho Yaida,Boris Hanin
Publisher : Cambridge University Press
Page : 473 pages
File Size : 55,8 Mb
Release : 2022-05-26
Category : Computers
ISBN : 9781316519332

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The Principles of Deep Learning Theory by Daniel A. Roberts,Sho Yaida,Boris Hanin Pdf

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

Fundamentals Of Deep Learning: Theory And Applications

Dr. Pokkuluri Kiran Sree,Mr. P J R Shalem Raju,Mrs. P. Archana,Dr. Raja Rao PBV
Fundamentals Of Deep Learning Theory And Applications Book PDF

Author : Dr. Pokkuluri Kiran Sree,Mr. P J R Shalem Raju,Mrs. P. Archana,Dr. Raja Rao PBV
Publisher : Academic Guru Publishing House
Page : 208 pages
File Size : 51,7 Mb
Release : 2023-03-29
Category : Study Aids
ISBN : 9788119152537

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Fundamentals Of Deep Learning: Theory And Applications by Dr. Pokkuluri Kiran Sree,Mr. P J R Shalem Raju,Mrs. P. Archana,Dr. Raja Rao PBV Pdf

Deep learning, often known as DL, is an approach to machine learning that is increasingly seen as the way of the future. Because of its impressive power of learning high-level abstract characteristics from enormous amounts of data, DL garners a lot of interest and also has a lot of success in pattern recognition, computer vision, data mining, and knowledge discovery. This is why DL is so successful in these areas. This book will not only seek to give a basic roadmap or direction to the existing deep learning approaches, but it will also highlight the problems and imagine fresh views that can lead to additional advancements in this subject. One of the most talked about topics in data science today is deep learning. Deep learning is a subfield of machine learning that makes use of sophisticated algorithms that take their cues from the way our own neural networks are wired and operate. The goal of this book is to provide a thorough introduction to deep learning, including an examination of its underlying algorithms, a presentation of its most recent theoretical advancements, a discussion of the most popular deep learning platforms and data sets, and an account of the significant advances made by a wide range of deep learning methodologies in areas such as text, video, image, speech, and audio processing.

Deep Learning Theory and Applications

Donatello Conte,Ana Fred,Oleg Gusikhin,Carlo Sansone
Deep Learning Theory and Applications Book PDF

Author : Donatello Conte,Ana Fred,Oleg Gusikhin,Carlo Sansone
Publisher : Springer Nature
Page : 496 pages
File Size : 50,5 Mb
Release : 2023-07-30
Category : Computers
ISBN : 9783031390593

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Deep Learning Theory and Applications by Donatello Conte,Ana Fred,Oleg Gusikhin,Carlo Sansone Pdf

This book consitiutes the refereed proceedings of the 4th International Conference on Deep Learning Theory and Applications, DeLTA 2023, held in Rome, Italy from 13 to 14 July 2023. The 9 full papers and 22 short papers presented were thoroughly reviewed and selected from the 42 qualified submissions. The scope of the conference includes such topics as models and algorithms; machine learning; big data analytics; computer vision applications; and natural language understanding.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 40,6 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing

Gyanendra Verma,Rajesh Doriya
Deep Learning Theory Architectures and Applications in Speech Image and Language Processing Book PDF

Author : Gyanendra Verma,Rajesh Doriya
Publisher : Bentham Science Publishers
Page : 270 pages
File Size : 42,9 Mb
Release : 2023-08-21
Category : Computers
ISBN : 9789815079227

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Deep Learning: Theory, Architectures and Applications in Speech, Image and Language Processing by Gyanendra Verma,Rajesh Doriya Pdf

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Innovations in Machine Learning

Dawn E. Holmes
Innovations in Machine Learning Book PDF

Author : Dawn E. Holmes
Publisher : Springer
Page : 285 pages
File Size : 49,9 Mb
Release : 2006-02-28
Category : Technology & Engineering
ISBN : 9783540334866

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Innovations in Machine Learning by Dawn E. Holmes Pdf

Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.

An Intuitive Exploration of Artificial Intelligence

Simant Dube
An Intuitive Exploration of Artificial Intelligence Book PDF

Author : Simant Dube
Publisher : Springer Nature
Page : 355 pages
File Size : 48,9 Mb
Release : 2021-06-21
Category : Computers
ISBN : 9783030686246

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An Intuitive Exploration of Artificial Intelligence by Simant Dube Pdf

This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.

Learning Deep Learning

Magnus Ekman
Learning Deep Learning Book PDF

Author : Magnus Ekman
Publisher : Addison-Wesley Professional
Page : 1105 pages
File Size : 51,8 Mb
Release : 2021-07-19
Category : Computers
ISBN : 9780137470297

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Learning Deep Learning by Magnus Ekman Pdf

NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results "To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals." -- From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA "Ekman uses a learning technique that in our experience has proven pivotal to success—asking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us." -- From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer. He describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generating natural language descriptions of images. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow with Keras. Corresponding PyTorch examples are provided online, and the book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning. Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how DL frameworks make it easier to develop more complicated and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences Master NLP with sequence-to-sequence networks and the Transformer architecture Build applications for natural language translation and image captioning NVIDIA's invention of the GPU sparked the PC gaming market. The company's pioneering work in accelerated computing--a supercharged form of computing at the intersection of computer graphics, high-performance computing, and AI--is reshaping trillion-dollar industries, such as transportation, healthcare, and manufacturing, and fueling the growth of many others. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Deep Learning Theory and Applications

Ana Fred,Carlo Sansone,Kurosh Madani
Deep Learning Theory and Applications Book PDF

Author : Ana Fred,Carlo Sansone,Kurosh Madani
Publisher : Springer Nature
Page : 163 pages
File Size : 43,6 Mb
Release : 2023-07-06
Category : Computers
ISBN : 9783031373206

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Deep Learning Theory and Applications by Ana Fred,Carlo Sansone,Kurosh Madani Pdf

This book constitutes the refereed post-proceedings of the First International Conference and Second International Conference on Deep Learning Theory and Applications, DeLTA 2020 and DeLTA 2021, was held virtually due to the COVID-19 crisis on July 8-10, 2020 and July 7–9, 2021. The 7 full papers included in this book were carefully reviewed and selected from 58 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structuredand unstructured multimodal data sources, natural language understanding andtranslation, and many other application domains.

Deep Learning Theory and Applications

Ana Fred,Carlo Sansone,Oleg Gusikhin,Kurosh Madani
Deep Learning Theory and Applications Book PDF

Author : Ana Fred,Carlo Sansone,Oleg Gusikhin,Kurosh Madani
Publisher : Springer Nature
Page : 130 pages
File Size : 52,7 Mb
Release : 2023-07-06
Category : Computers
ISBN : 9783031373176

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Deep Learning Theory and Applications by Ana Fred,Carlo Sansone,Oleg Gusikhin,Kurosh Madani Pdf

This book constitutes the refereed post-conference proceedings of the Third International Conference on Deep Learning Theory and Applications, DeLTA 2022, held in Lisbon, Portugal, during January 17-18, 2022. The 6 full papers included in this book were carefully reviewed and selected from 36 submissions. They present recent research on machine learning and artificial intelligence in real-world applications such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains.

Generative Adversarial Networks and Deep Learning

Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil
Generative Adversarial Networks and Deep Learning Book PDF

Author : Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil
Publisher : CRC Press
Page : 286 pages
File Size : 47,6 Mb
Release : 2023-04-10
Category : Computers
ISBN : 9781000840568

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Generative Adversarial Networks and Deep Learning by Roshani Raut,Pranav D Pathak,Sachin R Sakhare,Sonali Patil Pdf

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 GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The 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

Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 53,6 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

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.

Metaheuristics in Machine Learning: Theory and Applications

Diego Oliva
Metaheuristics in Machine Learning Theory and Applications Book PDF

Author : Diego Oliva
Publisher : Springer Nature
Page : 765 pages
File Size : 52,5 Mb
Release : 2024-07-03
Category : Computational intelligence
ISBN : 9783030705428

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Metaheuristics in Machine Learning: Theory and Applications by Diego Oliva Pdf

This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.