Theory And Applications In Deep Learning

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Information Bottleneck

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Author : Bernhard Geiger
language : en
Publisher:
Release Date : 2021

Information Bottleneck written by Bernhard Geiger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information-theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.

Deep Learning Explained Theory Applications And Future Directions

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Author : ASHISH KUMAR , ABHISHEK DAS, SHYAMAKRISHNA SIDDHARTH CHAMARTHY, PROF. (DR) PUNIT GOEL
language : en
Publisher: DeepMisti Publication
Release Date : 2024-10-19

Deep Learning Explained Theory Applications And Future Directions written by ASHISH KUMAR , ABHISHEK DAS, SHYAMAKRISHNA SIDDHARTH CHAMARTHY, PROF. (DR) PUNIT GOEL and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-19 with Computers categories.

In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Deep Learning Explained: Theory, Applications, and Future Directions, is conceived to bridge the gap between emerging technological advancements in artificial intelligence and their strategic application across various industries. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of deep learning technologies, from foundational theories to advanced applications. We delve into the critical aspects that drive successful AI innovations in fields such as healthcare, finance, e-commerce, and autonomous systems. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, researchers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and AI adoption to the strategic management of deep learning innovations. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that deep learning and AI technologies play in shaping the future of industries and businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how deep learning can be harnessed to drive future innovations. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating innovative solutions that will shape the future of technology. Thank you for joining us on this journey. Authors

Deep Learning Fundamentals Theory And Applications

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Author : Kaizhu Huang
language : en
Publisher: Springer
Release Date : 2019-02-15

Deep Learning Fundamentals Theory And Applications written by Kaizhu Huang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-15 with Medical categories.

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.

An Intuitive Exploration Of Artificial Intelligence

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Author : Simant Dube
language : en
Publisher:
Release Date : 2021

An Intuitive Exploration Of Artificial Intelligence written by Simant Dube and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.

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.

Deep Learning

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Author : Kaizhu Huang
language : en
Publisher:
Release Date : 2019

Deep Learning written by Kaizhu Huang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with COMPUTERS categories.

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.

Deep Learning Fundamentals Theory And Applications

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Author : Dr. R. Kanagaraj
language : en
Publisher: AG PUBLISHING HOUSE (AGPH Books)
Release Date :

Deep Learning Fundamentals Theory And Applications written by Dr. R. Kanagaraj and has been published by AG PUBLISHING HOUSE (AGPH Books) this book supported file pdf, txt, epub, kindle and other format this book has been release on with Study Aids categories.

More complex computing approaches have grown in popularity as technology has improved and big data has emerged. Increasing customer demand for better goods, as well as firms trying to better exploit their resources, have been driving this trend. Machine learning is a field that combines statistics, mathematics, and computer science to create and analyze algorithms that improve their own behavior in an iterative fashion by design. Initially, the discipline was committed to the development of artificial intelligence, but owing to the constraints of theory and technology at the time, it became more reasonable to concentrate these algorithms on particular tasks. Deep learning is a sort of machine learning and artificial intelligence (AI) that mimics how people acquire certain types of knowledge. Deep learning is a critical component of data science, which also covers statistics and predictive modeling. Deep learning is particularly advantageous to data scientists who are responsible with gathering, analyzing, and interpreting massive volumes of data; deep learning speeds up and simplifies this process. In this book the concept of deep learning under the machine learning is explained in every aspect. Whether, it's their fundamental concepts or the application of deep learning on daily basis.

Intelligent Computing Theories And Application

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Author : De-Shuang Huang
language : en
Publisher: Springer
Release Date : 2017-07-18

Intelligent Computing Theories And Application written by De-Shuang Huang and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-18 with Computers categories.

This three-volume set LNCS 10361, LNCS 10362, and LNAI 10363 constitutes the refereed proceedings of the 13th International Conference on Intelligent Computing, ICIC 2017, held in Liverpool, UK, in August 2017. The 212 full papers and 20 short papers of the three proceedings volumes were carefully reviewed and selected from 612 submissions. This first volume of the set comprises 71 papers. The papers are organized in topical sections such as Evolutionary Computation and Learning; Neural Networks; Nature Inspired Computing and Optimization; Signal Processing; Pattern Recognition; Biometrics Recognition; Image Processing; Information Security; Virtual Reality and Human-Computer Interaction; Business Intelligence and Multimedia Technology; Genetic Algorithms; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Swarm Intelligence and Optimization; Independent Component Analysis; Compressed Sensing and Sparse Coding; Natural Computing; Intelligent Computing in Computer Vision; Computational Intelligence and Security for Image Applications in Social Network; Neural Networks: Theory and Application.

Deep Learning Theory Architectures And Applications In Speech Image And Language Processing

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Author : Gyanendra Verma
language : en
Publisher: Bentham Science Publishers
Release Date : 2023-08-21

Deep Learning Theory Architectures And Applications In Speech Image And Language Processing written by Gyanendra Verma and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-21 with Computers categories.

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.

Deep Learning And Neural Networks Concepts Methodologies Tools And Applications

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Author : Management Association, Information Resources
language : en
Publisher: IGI Global
Release Date : 2019-10-11

Deep Learning And Neural Networks Concepts Methodologies Tools And Applications written by Management Association, Information Resources and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-11 with Computers categories.

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Bio Inspired Computing Theories And Applications

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Author : Linqiang Pan
language : en
Publisher: Springer Nature
Release Date : 2024-04-15

Bio Inspired Computing Theories And Applications written by Linqiang Pan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-15 with Computers categories.

The two-volume set CCIS 2061 and 2062 constitutes the refereed post-conference proceedings of the 18th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2023, held in Changsha, China, during December 15–17, 2023. The 64 revised full papers presented in these proceedings were carefully reviewed and selected from 168 submissions. The papers are organized in the following topical sections: Volume I: Evolutionary Computation and Swarm Intelligence; and Membrane Computing and DNA Computing Volume II: Machine Learning and Applications; and Intelligent Control and Application.