# Deep Learning for Data Analytics

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## Deep Learning in Data Analytics

Author | : Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman |

Publsiher | : Springer |

Total Pages | : 266 |

Release | : 2021-09-21 |

ISBN 10 | : 9783030758547 |

ISBN 13 | : 3030758540 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning in Data Analytics Book Review:**

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.

## Deep Learning for Data Analytics

Author | : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey |

Publsiher | : Academic Press |

Total Pages | : 218 |

Release | : 2020-05-29 |

ISBN 10 | : 0128226080 |

ISBN 13 | : 9780128226087 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning for Data Analytics Book Review:**

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

Author | : Debi Prasanna Acharjya |

Publsiher | : Springer Nature |

Total Pages | : 271 |

Release | : 2021 |

ISBN 10 | : 3030758559 |

ISBN 13 | : 9783030758554 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning in Data Analytics Book Review:**

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.

## Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Author | : R. Sujatha,S. L. Aarthy,R. Vettriselvan |

Publsiher | : CRC Press |

Total Pages | : 216 |

Release | : 2021-09-23 |

ISBN 10 | : 1000454541 |

ISBN 13 | : 9781000454543 |

Language | : EN, FR, DE, ES & NL |

**Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Book Review:**

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.

## Fundamentals of Machine Learning for Predictive Data Analytics second edition

Author | : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy |

Publsiher | : MIT Press |

Total Pages | : 856 |

Release | : 2020-10-20 |

ISBN 10 | : 0262361108 |

ISBN 13 | : 9780262361101 |

Language | : EN, FR, DE, ES & NL |

**Fundamentals of Machine Learning for Predictive Data Analytics second edition Book Review:**

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.

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

Author | : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh |

Publsiher | : CRC Press |

Total Pages | : 266 |

Release | : 2020-10-08 |

ISBN 10 | : 1000179532 |

ISBN 13 | : 9781000179538 |

Language | : EN, FR, DE, ES & NL |

**Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches Book Review:**

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

## Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author | : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian |

Publsiher | : IGI Global |

Total Pages | : 355 |

Release | : 2019-11-29 |

ISBN 10 | : 1799811948 |

ISBN 13 | : 9781799811947 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book Review:**

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.

## Nature Inspired Algorithms for Big Data Frameworks

Author | : Banati, Hema,Mehta, Shikha,Kaur, Parmeet |

Publsiher | : IGI Global |

Total Pages | : 412 |

Release | : 2018-09-28 |

ISBN 10 | : 1522558535 |

ISBN 13 | : 9781522558538 |

Language | : EN, FR, DE, ES & NL |

**Nature Inspired Algorithms for Big Data Frameworks Book Review:**

As technology continues to become more sophisticated, mimicking natural processes and phenomena becomes more of a reality. Continued research in the field of natural computing enables an understanding of the world around us, in addition to opportunities for manmade computing to mirror the natural processes and systems that have existed for centuries. Nature-Inspired Algorithms for Big Data Frameworks is a collection of innovative research on the methods and applications of extracting meaningful information from data using algorithms that are capable of handling the constraints of processing time, memory usage, and the dynamic and unstructured nature of data. Highlighting a range of topics including genetic algorithms, data classification, and wireless sensor networks, this book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the application of nature and biologically inspired algorithms for handling challenges posed by big data in diverse environments.

## Deep Learning Convergence to Big Data Analytics

Author | : Murad Khan,Bilal Jan,Haleem Farman |

Publsiher | : Springer |

Total Pages | : 79 |

Release | : 2018-12-30 |

ISBN 10 | : 9811334595 |

ISBN 13 | : 9789811334597 |

Language | : EN, FR, DE, ES & NL |

**Deep Learning Convergence to Big Data Analytics Book Review:**

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.

## Machine Learning and Data Analytics for Predicting Managing and Monitoring Disease

Author | : Roy, Manikant,Gupta, Lovi Raj |

Publsiher | : IGI Global |

Total Pages | : 241 |

Release | : 2021-06-25 |

ISBN 10 | : 1799871908 |

ISBN 13 | : 9781799871903 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Data Analytics for Predicting Managing and Monitoring Disease Book Review:**

Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

## Data Analytics

Author | : Anthony S. Williams |

Publsiher | : Anthony S. Williams |

Total Pages | : 439 |

Release | : 2020-02-12 |

ISBN 10 | : 1928374650XXX |

ISBN 13 | : 9182736450XXX |

Language | : EN, FR, DE, ES & NL |

**Data Analytics Book Review:**

Data Analytics - 7 BOOK BUNDLE!! Book 1: Data Analytics For Beginners In this book you will learn: What is Data Analytics Types of Data Analytics Evolution of Data Analytics Big Data Defined Data Mining Data Visualization Cluster Analysis 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 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 And of course much more! Book 4: Reinforcement Learning With Python In this book you will learn: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search And much, much more... Book 5: Artificial Intelligence Python In this book you will learn: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning And much, much more... Book 6: Text Analytics With Python In this book you will learn: Text analytics process How to build a corpus and analyze sentiment Named entity extraction with Groningen meaning bank corpus How to train your system Getting started with NLTK How to search syntax and tokenize sentences Automatic text summarization Stemming word and topic modeling with NLTK And much, much more... Book 7: 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 And of course much more! Download this book bundle NOW and SAVE money!!

## Practical Machine Learning for Data Analysis Using Python

Author | : Abdulhamit Subasi |

Publsiher | : Academic Press |

Total Pages | : 534 |

Release | : 2020-06-05 |

ISBN 10 | : 0128213809 |

ISBN 13 | : 9780128213803 |

Language | : EN, FR, DE, ES & NL |

**Practical Machine Learning for Data Analysis Using Python Book Review:**

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

## Big Data Analysis and Deep Learning Applications

Author | : Thi Thi Zin,Jerry Chun-Wei Lin |

Publsiher | : Springer |

Total Pages | : 386 |

Release | : 2018-06-06 |

ISBN 10 | : 9811308691 |

ISBN 13 | : 9789811308697 |

Language | : EN, FR, DE, ES & NL |

**Big Data Analysis and Deep Learning Applications Book Review:**

This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.

## Feature Engineering for Machine Learning and Data Analytics

Author | : Guozhu Dong,Huan Liu |

Publsiher | : CRC Press |

Total Pages | : 400 |

Release | : 2018-03-14 |

ISBN 10 | : 1351721275 |

ISBN 13 | : 9781351721271 |

Language | : EN, FR, DE, ES & NL |

**Feature Engineering for Machine Learning and Data Analytics Book Review:**

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.

## Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Author | : Om Prakash Jena,Bharat Bhushan,Utku Kose |

Publsiher | : CRC Press |

Total Pages | : 280 |

Release | : 2022 |

ISBN 10 | : 9781032126876 |

ISBN 13 | : 1032126876 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications Book Review:**

This book introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary ML/DL research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for healthcare sector, it depth, breadth, complexity, and diversity of this multi-disciplinary area. This book provides a comprehensive overview of Machine Learning (ML) and Deep Learning (DL) algorithms and explores the related use cases in enterprises such as computer aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. The book aims to endow different communities with their innovative advances in theory, analytical results, case studies, numerical simulation, modelling, and computational structuring in the field of ML/DL models for healthcare applications. This book will reveal different dimensions of ML/DL applications and will illustrate its use in the solution of assorted real world biomedical and healthcare problems. This book is a valuable source for information for researchers, scientists, healthcare professional, programmers and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios.

## Advanced Data Analytics Using Python

Author | : Sayan Mukhopadhyay |

Publsiher | : Apress |

Total Pages | : 186 |

Release | : 2018-03-29 |

ISBN 10 | : 1484234502 |

ISBN 13 | : 9781484234501 |

Language | : EN, FR, DE, ES & NL |

**Advanced Data Analytics Using Python Book Review:**

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.

## Fundamentals of Machine Learning for Predictive Data Analytics second edition

Author | : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy |

Publsiher | : MIT Press |

Total Pages | : 856 |

Release | : 2020-10-20 |

ISBN 10 | : 0262044692 |

ISBN 13 | : 9780262044691 |

Language | : EN, FR, DE, ES & NL |

**Fundamentals of Machine Learning for Predictive Data Analytics second edition Book Review:**

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. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.

## Machine Learning and Big Data Analytics Paradigms Analysis Applications and Challenges

Author | : Aboul Ella Hassanien,Ashraf Darwish |

Publsiher | : Springer Nature |

Total Pages | : 648 |

Release | : 2020-12-14 |

ISBN 10 | : 303059338X |

ISBN 13 | : 9783030593384 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Big Data Analytics Paradigms Analysis Applications and Challenges Book Review:**

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.

## Advanced Deep Learning Applications in Big Data Analytics

Author | : Bouarara, Hadj Ahmed |

Publsiher | : IGI Global |

Total Pages | : 351 |

Release | : 2020-10-16 |

ISBN 10 | : 1799827933 |

ISBN 13 | : 9781799827931 |

Language | : EN, FR, DE, ES & NL |

**Advanced Deep Learning Applications in Big Data Analytics Book Review:**

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.

## Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Author | : R. Sujatha,S. L. Aarthy,R. Vettriselvan |

Publsiher | : CRC Press |

Total Pages | : 216 |

Release | : 2021-09-23 |

ISBN 10 | : 1000454541 |

ISBN 13 | : 9781000454543 |

Language | : EN, FR, DE, ES & NL |

**Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics Book Review:**

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.