Deep Learning Algorithms And Applications

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Deep Learning: Algorithms and Applications

Witold Pedrycz,Shyi-Ming Chen
Deep Learning Algorithms and Applications Book PDF

Author : Witold Pedrycz,Shyi-Ming Chen
Publisher : Springer Nature
Page : 360 pages
File Size : 52,9 Mb
Release : 2019-10-23
Category : Technology & Engineering
ISBN : 9783030317607

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Deep Learning: Algorithms and Applications by Witold Pedrycz,Shyi-Ming Chen Pdf

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.

Machine and Deep Learning Algorithms and Applications

Uday Shankar,Andreas Spanias
Machine and Deep Learning Algorithms and Applications Book PDF

Author : Uday Shankar,Andreas Spanias
Publisher : Springer Nature
Page : 107 pages
File Size : 43,6 Mb
Release : 2022-05-31
Category : Technology & Engineering
ISBN : 9783031037580

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Machine and Deep Learning Algorithms and Applications by Uday Shankar,Andreas Spanias Pdf

This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning to address a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Machine Learning Algorithms and Applications

Mettu Srinivas,G. Sucharitha,Anjanna Matta
Machine Learning Algorithms and Applications Book PDF

Author : Mettu Srinivas,G. Sucharitha,Anjanna Matta
Publisher : John Wiley & Sons
Page : 372 pages
File Size : 48,9 Mb
Release : 2021-08-10
Category : Computers
ISBN : 9781119769248

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Machine Learning Algorithms and Applications by Mettu Srinivas,G. Sucharitha,Anjanna Matta Pdf

Machine Learning Algorithms is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the user can easily move from the equations in the book to a computer program.

Deep Learning

Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy
Deep Learning Book PDF

Author : Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy
Publisher : Walter de Gruyter GmbH & Co KG
Page : 161 pages
File Size : 48,7 Mb
Release : 2020-06-22
Category : Computers
ISBN : 9783110670905

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Deep Learning by Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy Pdf

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Machine Learning

Anonim
Machine Learning Book PDF

Author : Anonim
Publisher : BoD – Books on Demand
Page : 153 pages
File Size : 41,9 Mb
Release : 2021-12-22
Category : Computers
ISBN : 9781839694844

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Machine Learning by Anonim Pdf

Recent times are witnessing rapid development in machine learning algorithm systems, especially in reinforcement learning, natural language processing, computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of machine learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of deep learning and artificial intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation. The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of machine learning, deep learning, and artificial intelligence.

Machine Learning

Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier
Machine Learning Book PDF

Author : Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier
Publisher : CRC Press
Page : 227 pages
File Size : 49,5 Mb
Release : 2016-08-19
Category : Computers
ISBN : 9781498705394

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Machine Learning by Mohssen Mohammed,Muhammad Badruddin Khan,Eihab Bashier Mohammed Bashier Pdf

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Trends in Deep Learning Methodologies

Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava
Trends in Deep Learning Methodologies Book PDF

Author : Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava
Publisher : Academic Press
Page : 308 pages
File Size : 42,7 Mb
Release : 2020-11-12
Category : Computers
ISBN : 9780128232682

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Trends in Deep Learning Methodologies by Vincenzo Piuri,Sandeep Raj,Angelo Genovese,Rajshree Srivastava Pdf

Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. Provides insights into the theory, algorithms, implementation and the application of deep learning techniques Covers a wide range of applications of deep learning across smart healthcare and smart engineering Investigates the development of new models and how they can be exploited to find appropriate solutions

Deep Learning Applications

M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh
Deep Learning Applications Book PDF

Author : M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh
Publisher : Springer Nature
Page : 184 pages
File Size : 52,6 Mb
Release : 2020-02-28
Category : Technology & Engineering
ISBN : 9789811518164

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Deep Learning Applications by M. Arif Wani,Mehmed Kantardzic,Moamar Sayed-Mouchaweh Pdf

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Handbook of Deep Learning Applications

Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
Handbook of Deep Learning Applications Book PDF

Author : Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui
Publisher : Springer
Page : 383 pages
File Size : 51,7 Mb
Release : 2019-02-25
Category : Technology & Engineering
ISBN : 9783030114794

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Handbook of Deep Learning Applications by Valentina Emilia Balas,Sanjiban Sekhar Roy,Dharmendra Sharma,Pijush Samui Pdf

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Deep Neural Networks

Yunong Zhang,Dechao Chen,Chengxu Ye
Deep Neural Networks Book PDF

Author : Yunong Zhang,Dechao Chen,Chengxu Ye
Publisher : CRC Press
Page : 340 pages
File Size : 41,9 Mb
Release : 2019-03-19
Category : Business & Economics
ISBN : 9780429760990

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Deep Neural Networks by Yunong Zhang,Dechao Chen,Chengxu Ye Pdf

Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors’ 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet, and allows reader to extend the techniques in the book to solve scientific and engineering problems. The book will be of interest to engineers, senior undergraduates, postgraduates, and researchers in the fields of neuronets, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, simulation and modeling, deep learning, and data mining. Features Focuses on neuronet models, algorithms, and applications Designs, constructs, develops, analyzes, simulates and compares various WASD neuronet models, such as single-input WASD neuronet models, two-input WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations Includes real-world applications, such as population prediction Provides complete mathematical foundations, such as Weierstrass approximation, Bernstein polynomial approximation, Taylor polynomial approximation, and multivariate function approximation, exploring the close integration of mathematics (i.e., function approximation theories) and computers (e.g., computer algorithms) Utilizes the authors' 20 years of research on neuronets

Data Science

Gyanendra K. Verma,Badal Soni,Salah Bourennane,Alexandre C. B. Ramos
Data Science Book PDF

Author : Gyanendra K. Verma,Badal Soni,Salah Bourennane,Alexandre C. B. Ramos
Publisher : Unknown
Page : 0 pages
File Size : 51,9 Mb
Release : 2021
Category : Electronic
ISBN : 9811616825

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Data Science by Gyanendra K. Verma,Badal Soni,Salah Bourennane,Alexandre C. B. Ramos Pdf

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.

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 : 45,6 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.

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

R. Sujatha,S. L. Aarthy,R. Vettriselvan
Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Book PDF

Author : R. Sujatha,S. L. Aarthy,R. Vettriselvan
Publisher : CRC Press
Page : 216 pages
File Size : 47,7 Mb
Release : 2021-09-22
Category : Technology & Engineering
ISBN : 9781000454536

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Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by R. Sujatha,S. L. Aarthy,R. Vettriselvan Pdf

Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

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 : 44,7 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 For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications

Xiang Zhang,Lina Yao
Deep Learning For Eeg based Brain computer Interfaces Representations Algorithms And Applications Book PDF

Author : Xiang Zhang,Lina Yao
Publisher : World Scientific
Page : 294 pages
File Size : 50,9 Mb
Release : 2021-09-14
Category : Computers
ISBN : 9781786349606

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Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications by Xiang Zhang,Lina Yao Pdf

Deep Learning for EEG-Based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices.This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI data sets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.Related Link(s)