Deep Learning For Data Analytics

Deep Learning for Data Analytics
Publisher Academic Press
Release Date
Category Science
Total Pages 218
ISBN 9780128226087
Rating 4/5 from 21 reviews
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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
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2020-05-29

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

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Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2020-07-02

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

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

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.

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Deep Learning  Convergence to Big Data Analytics
  • Author : Murad Khan,Bilal Jan,Haleem Farman
  • Publisher : Springer
  • Release Date : 2018-12-30

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

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

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

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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

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

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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

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,

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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 : 2020-10-08

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

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Advanced Data Analytics Using Python
  • Author : Sayan Mukhopadhyay
  • Publisher : Apress
  • Release Date : 2018-03-29

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

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Data Analytics and AI
  • Author : Jay Liebowitz
  • Publisher : CRC Press
  • Release Date : 2020-08-06

Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years

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Big Data Analysis and Deep Learning Applications
  • Author : Thi Thi Zin,Jerry Chun-Wei Lin
  • Publisher : Springer
  • Release Date : 2018-06-06

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

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Feature Engineering for Machine Learning and Data Analytics
  • Author : Guozhu Dong,Huan Liu
  • Publisher : CRC Press
  • Release Date : 2018-03-14

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

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

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

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Fundamentals of Machine Learning for Predictive Data Analytics
  • Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
  • Publisher : MIT Press
  • Release Date : 2015-07-24

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

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Applications of Machine Learning in Big Data Analytics and Cloud Computing
  • Author : Subhendu Kumar Pani,Somanath Tripathy,George Jandieri,Sumit Kundu,Talal Ashraf Butt
  • Publisher : River Publishers Information S
  • Release Date : 2020-07-30

Cloud Computing and Big Data technologies have become the new descriptors of the digital age. The global amount of digital data has increased more than nine times in volume in just five years and by 2030 its volume may reach a staggering 65 trillion gigabytes. This explosion of data has led to

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