Deep Learning for Data Analytics

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  • Publisher : Academic Press
  • Release : 29 May 2020
  • ISBN : 9780128226087
  • Page : 218 pages
  • Rating : 4.5/5 from 103 voters

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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

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Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2020-05-29
  • ISBN : 9780128226087
GET THIS BOOKDeep Learning for Data Analytics

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

Deep Learning in Data Analytics

Deep Learning in Data Analytics
  • Author : Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman
  • Publisher : Springer
  • Release Date : 2021-09-21
  • ISBN : 3030758540
GET THIS BOOKDeep Learning in Data Analytics

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

Deep Learning in Data Analytics

Deep Learning in Data Analytics
  • Author : Debi Prasanna Acharjya,Anirban Mitra,Noor Zaman
  • Publisher : Springer Nature
  • Release Date : 2021-08-11
  • ISBN : 9783030758554
GET THIS BOOKDeep Learning in Data Analytics

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

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
  • Author : K. Gayathri Devi,Mamata Rath,Nguyen Thi Dieu Linh
  • Publisher : CRC Press
  • Release Date : 2020-10-07
  • ISBN : 9781000179514
GET THIS BOOKArtificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

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

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
  • Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
  • Publisher : MIT Press
  • Release Date : 2020-10-20
  • ISBN : 9780262361101
GET THIS BOOKFundamentals of Machine Learning for Predictive Data Analytics, second edition

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

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
  • Author : Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
  • Publisher : IGI Global
  • Release Date : 2019-11-29
  • ISBN : 9781799811947
GET THIS BOOKDeep Learning Techniques and Optimization Strategies in Big Data Analytics

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

Nature-Inspired Algorithms for Big Data Frameworks

Nature-Inspired Algorithms for Big Data Frameworks
  • Author : Banati, Hema,Mehta, Shikha,Kaur, Parmeet
  • Publisher : IGI Global
  • Release Date : 2018-09-28
  • ISBN : 9781522558538
GET THIS BOOKNature-Inspired Algorithms for Big Data Frameworks

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

Advanced Data Analytics Using Python

Advanced Data Analytics Using Python
  • Author : Sayan Mukhopadhyay
  • Publisher : Apress
  • Release Date : 2018-03-29
  • ISBN : 9781484234501
GET THIS BOOKAdvanced Data Analytics Using Python

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

Advanced Deep Learning Applications in Big Data Analytics

Advanced Deep Learning Applications in Big Data Analytics
  • Author : Bouarara, Hadj Ahmed
  • Publisher : IGI Global
  • Release Date : 2020-10-16
  • ISBN : 9781799827931
GET THIS BOOKAdvanced Deep Learning Applications in Big Data Analytics

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

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics
  • Author : R. Sujatha,S. L. Aarthy,R. Vettriselvan
  • Publisher : CRC Press
  • Release Date : 2021-09-22
  • ISBN : 9781000454536
GET THIS BOOKIntegrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

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,

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

Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
  • Author : Aboul Ella Hassanien,Ashraf Darwish
  • Publisher : Springer Nature
  • Release Date : 2020-12-14
  • ISBN : 9783030593384
GET THIS BOOKMachine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

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.

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

Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease
  • Author : Roy, Manikant,Gupta, Lovi Raj
  • Publisher : IGI Global
  • Release Date : 2021-06-25
  • ISBN : 9781799871903
GET THIS BOOKMachine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease

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

Machine Learning for Data Streams

Machine Learning for Data Streams
  • Author : Albert Bifet,Ricard Gavalda,Geoff Holmes,Bernhard Pfahringer
  • Publisher : MIT Press
  • Release Date : 2018-03-16
  • ISBN : 9780262346054
GET THIS BOOKMachine Learning for Data Streams

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining

Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2020-06-05
  • ISBN : 9780128213803
GET THIS BOOKPractical Machine Learning for Data Analysis Using Python

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

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics
  • Author : Pradeep N,Sandeep Kautish,Sheng-Lung Peng
  • Publisher : Academic Press
  • Release Date : 2021-06-25
  • ISBN : 9780128220443
GET THIS BOOKDemystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision