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Deep Learning for Data Analytics

Deep Learning for Data Analytics
Foundations, Biomedical Applications, and Challenges

by Himansu Das,Chittaranjan Pradhan,Nilanjan Dey

  • Publisher : Academic Press
  • Release : 2020-05-29
  • Pages : 218
  • ISBN : 0128226080
  • Language : En, Es, Fr & De
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Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

Deep Learning in Data Analytics

Deep Learning in Data Analytics
Recent Techniques, Practices and Applications

by Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman

  • Publisher : Springer
  • Release : 2021-08-02
  • Pages : 595
  • ISBN : 9783030758547
  • Language : En, Es, Fr & De
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This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
A Book

by Aboul Ella Hassanien,Ashraf Darwish

  • Publisher : Springer Nature
  • Release : 2020-12-14
  • Pages : 648
  • ISBN : 303059338X
  • Language : En, Es, Fr & De
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This book is intended to present the state of the art in research on machine learning and big data analytics. The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
A Book

by Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian

  • Publisher : IGI Global
  • Release : 2019-11-29
  • Pages : 355
  • ISBN : 1799811948
  • Language : En, Es, Fr & De
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Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Advanced Deep Learning Applications in Big Data Analytics

Advanced Deep Learning Applications in Big Data Analytics
A Book

by Bouarara, Hadj Ahmed

  • Publisher : IGI Global
  • Release : 2020-10-16
  • Pages : 351
  • ISBN : 1799827933
  • Language : En, Es, Fr & De
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Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.

Deep Learning

Deep Learning
Machine Learning and Data Analytics Explained

by David Feldspar

  • Publisher : Independently Published
  • Release : 2018-02
  • Pages : 32
  • ISBN : 9781980285793
  • Language : En, Es, Fr & De
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How can deep learning, even machine learning, help your organization? The lofty expectations about machine learning and deep studies and projects have skyrocketed, and yet, there is so much left to be said about the methods that trigger the higher-functioning corners of the human neural networks. With so many data and investments on the line, how can we deepen our understanding of these subjects? That is where this guide will take you to the next level. It touches on exactly those problems and methods that optimize your financing and comprehension of the little details that often get overlooked. Furthermore, you will read about subtopics like: Popular machine learning methods that are being applied today. Data mining processes that you can easily use for your own company or individual proprietorship. Insights in supervised versus unsupervised data mining. Machine learning tactics and know-how. The five best steps to implement unsupervised big data machine learning. Ten ways to apply predictive analyses to the banking sector. Financial optimization techniques for regular processes. These machine learning, data mining, and other financing strategies are an intellectual, analytical goldmine you can feast your mind on

Fundamentals of Machine Learning for Predictive Data Analytics

Fundamentals of Machine Learning for Predictive Data Analytics
Algorithms, Worked Examples, and Case Studies

by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy

  • Publisher : MIT Press
  • Release : 2015-07-24
  • Pages : 624
  • ISBN : 0262029448
  • Language : En, Es, Fr & De
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A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches
A Book

by K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh

  • Publisher : CRC Press
  • Release : 2020-10-07
  • Pages : 250
  • ISBN : 1000179516
  • Language : En, Es, Fr & De
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Artificial Intelligence (AI), when incorporated with machine learning and deep learning algorithms, has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images, covers the automation of a system through machine learning and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural language processing, computer vision, and AI approaches in real-time applications. Academic scientists, researchers, and students in the various domains of computer science engineering, electronics and communication engineering, and information technology, as well as industrial engineers, biomedical engineers, and management, will find this book useful. By the end of this book, you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning

Machine Learning Cookbook with Python

Machine Learning Cookbook with Python
Create ML and Data Analytics Projects Using Some Amazing Open Datasets

by Rehan Guha

  • Publisher : BPB Publications
  • Release : 2020-11-12
  • Pages : 264
  • ISBN : 9389898005
  • Language : En, Es, Fr & De
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A Cookbook that will help you implement Machine Learning algorithms and techniques by building real-world projects KEY FEATURES Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics. Create Predictive Models and choose the right model for various types of Datasets. Learn the art of tuning a model to improve accuracy as per Business requirements. Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning. DESCRIPTION Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background. WHAT WILL YOU LEARN Understand the working of the O.S.E.M.N. framework in Data Science. Get familiar with the end-to-end implementation of Machine Learning Pipeline. Learn how to implement Machine Learning algorithms and concepts using Python. Learn how to build a Predictive Model for a Business case. WHO THIS BOOK IS FOR This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners. TABLE OF CONTENTS 1. Boston Crime 2. World Happiness Report 3. Iris Species 4. Credit Card Fraud Detection 5. Heart Disease UCI

Deep Learning: Convergence to Big Data Analytics

Deep Learning: Convergence to Big Data Analytics
A Book

by Murad Khan,Bilal Jan,Haleem Farman

  • Publisher : Springer
  • Release : 2018-12-30
  • Pages : 79
  • ISBN : 9811334595
  • Language : En, Es, Fr & De
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This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Algorithms, Worked Examples, and Case Studies

by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy

  • Publisher : MIT Press
  • Release : 2020-10-20
  • Pages : 856
  • ISBN : 0262361108
  • Language : En, Es, Fr & De
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The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Big Data

Big Data
4 Manuscripts - Data Analytics for Beginners, Deep Learning With Keras, Analyzing Data With Power Bi, Convolutional Neural Networks in Python

by Anthony Williams

  • Publisher : Createspace Independent Publishing Platform
  • Release : 2017-08-11
  • Pages : 370
  • ISBN : 9781974435562
  • Language : En, Es, Fr & De
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Big Data - 4 book BUNDLE!! Book 1: Data Analytics for Beginners In this book you will learn: Putting Data Analytics to Work The Rise of Data Analytics Big Data Defined Cluster Analysis Applications of Cluster Analysis Commonly Graphed Information Data Visualization Four Important Features of Data Visualization Software Big Data Impact Envisaged by 2020 Pros and Cons of Big Data Analytics And of course much more! Book 2: Deep Learning with Keras In this book you will learn: Deep Neural Network Neural Network Elements Keras Models Sequential Model Functional API Model Keras Layers Core Keras Layers Convolutional Keras Layers Recurrent Keras Layers Deep Learning Algorithms Supervised Learning Algorithms Applications of Deep Learning Models Automatic Speech and Image Recognition Natural Language Processing Video Game Development Real World Applications And of course much more! Book 3: Analyzing Data with Power BI In this book you will learn: Basics of data analysis processes Fundamental data analysis algorithms Basic of data and text mining, data visualization and business intelligence Techniques used for analysing quantitative data Basic data analysis tasks Conceptual, logical and physical data models Power BI service and data modelling Creating reports and visualizations in Power BI Data transformation and data cleaning in Power BI Real world applications of data analysis Book 4: Convolutional Neural Networks in Python In this book you will learn: Architecture of convolutional neural networks Solving computer vision tasks using convolutional neural networks Python and computer vision Automatic image and speech recognition Theano and TenroeFlow image recognition How to use MNIST vision dataset What are commonly used convolutional filters Get this book bundle NOW and SAVE money!! Buy the Paperback version AND get this ebook bundle for FREE!!

Data Analytics and Machine Learning Fundamentals LiveLessons Video Training

Data Analytics and Machine Learning Fundamentals LiveLessons Video Training
A Book

by Jerome Henry

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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More than 7.5 Hours of Video Instruction Overview Nearly every company in the world is evaluating its digital strategy and looking for ways to capitalize on the promise of digitization. Big data analytics and machine learning are central to this strategy. Understanding the fundamentals of data processing and artificial intelligence is becoming required knowledge for executives, digital architects, IT administrators, and operational telecom (OT) professionals in nearly every industry. In Data Analytics and Machine Learning Fundamentals LiveLessons , experienced CCIEs Robert Barton and Jerome Henry provide more than 7 1/2 hours of personal instruction exploring the principles of big data analytics, supervised learning, unsupervised learning, and neural networks. In addition to delving into the fundamental concepts, Barton and Henry address sample big data and machine learning use cases in different industries and present demos featuring the most common tools (such as Hadoop, TensorFlow, Matlab/Octave, R, and Python) in various fields used by data scientists and researchers. At the conclusion of this video course, you will be armed with knowledge and application skills required to become proficient in articulating big data analytics and machine learning principles and possibilities. Skill Level Beginner to intermediate data analytics/machine learning knowledge Learn How To * Understand how static and real-time streaming data is collected, analyzed, and used * Understand the key tools and methods that enable machines to learn and mimic human thinking * Bring together unstructured data in preparation for analysis and visualization * Compare and contrast the various big data architectures * Apply supervised learning/linear regression, data fitting, and reinforcement learning to machines to yield the information results you're looking for * Apply classification techniques to machine learning to better analyze your data * Exploit the benefits of unsupervised learning to glean data you didn't even know you were looking for * Understand how artificial neural networks (ANNs) perform deep learning with surprising (and useful) results * Apply principal components analysis (PCA) to improve the management of data analysis * Understand the key approaches to implementing machine learning on real systems and the considerations you must make when undertaking a machine learning project Who Should Take This Course * Anyone who wants to learn about machine learni...

Fog Computing, Deep Learning and Big Data Analytics-Research Directions

Fog Computing, Deep Learning and Big Data Analytics-Research Directions
A Book

by C.S.R. Prabhu

  • Publisher : Springer
  • Release : 2019-01-04
  • Pages : 71
  • ISBN : 9811332096
  • Language : En, Es, Fr & De
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This book provides a comprehensive picture of fog computing technology, including of fog architectures, latency aware application management issues with real time requirements, security and privacy issues and fog analytics, in wide ranging application scenarios such as M2M device communication, smart homes, smart vehicles, augmented reality and transportation management. This book explores the research issues involved in the application of traditional shallow machine learning and deep learning techniques to big data analytics. It surveys global research advances in extending the conventional unsupervised or clustering algorithms, extending supervised and semi-supervised algorithms and association rule mining algorithms to big data Scenarios. Further it discusses the deep learning applications of big data analytics to fields of computer vision and speech processing, and describes applications such as semantic indexing and data tagging. Lastly it identifies 25 unsolved research problems and research directions in fog computing, as well as in the context of applying deep learning techniques to big data analytics, such as dimensionality reduction in high-dimensional data and improved formulation of data abstractions along with possible directions for their solutions.

Data Analytics and Management

Data Analytics and Management
Proceedings of ICDAM

by Ashish Khanna

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 9811583358
  • Language : En, Es, Fr & De
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Big Data Analytics Methods

Big Data Analytics Methods
Analytics Techniques in Data Mining, Deep Learning and Natural Language Processing

by Peter Ghavami

  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 2019-12-16
  • Pages : 254
  • ISBN : 1547401567
  • Language : En, Es, Fr & De
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Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy

The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy
SPIoT-2020, Volume 1

by John MacIntyre

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 3030627438
  • Language : En, Es, Fr & De
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Learn Data Analytics For Beginners

Learn Data Analytics For Beginners
Data Analyst, Deep Learning, Neural Network, Python Data Analytics

by Landon Adrian

  • Publisher : Unknown Publisher
  • Release : 2019-08-11
  • Pages : 136
  • ISBN : 9781089671534
  • Language : En, Es, Fr & De
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Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming.Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects Reproducibility is the idea that data analyses should be published or made available with their data and software code so that others may verify the findings and build upon them. The need for reproducible report writing is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.

Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics
A Book

by Guozhu Dong,Huan Liu

  • Publisher : CRC Press
  • Release : 2018-03-14
  • Pages : 400
  • ISBN : 1351721267
  • Language : En, Es, Fr & De
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Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

Deep Learning Innovations and Their Convergence With Big Data

Deep Learning Innovations and Their Convergence With Big Data
A Book

by Karthik, S.,Paul, Anand,Karthikeyan, N.

  • Publisher : IGI Global
  • Release : 2017-07-13
  • Pages : 265
  • ISBN : 1522530169
  • Language : En, Es, Fr & De
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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.