Deep 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 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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  • Author : Himansu Das
  • Publisher : Academic Press
  • Pages : 218 pages
  • ISBN : 0128197641
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKDeep Learning for Data Analytics

Deep Learning for Data Analytics

Deep Learning for Data Analytics
  • Author : Himansu Das,Chittaranjan Pradhan,Nilanjan Dey
  • Publisher : Academic Press
  • Release : 02 July 2020
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 Nature
  • Release : 11 August 2021
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 : 07 October 2020
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

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 : 25 June 2021
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

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 : 20 October 2020
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

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 : 28 September 2018
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

Deep Learning in Data Analytics

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

Practical Machine Learning for Data Analysis Using Python

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

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 : 29 November 2019
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

Machine Learning for Data Streams

Machine Learning for Data Streams
  • Author : Albert Bifet,Ricard Gavalda,Geoff Holmes,Bernhard Pfahringer
  • Publisher : MIT Press
  • Release : 16 March 2018
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

Advanced Deep Learning Applications in Big Data Analytics

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

Machine Learning for Big Data Analysis

Machine Learning for Big Data Analysis
  • Author : Siddhartha Bhattacharyya,Hrishikesh Bhaumik,Anirban Mukherjee,Sourav De
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 17 December 2018
GET THIS BOOKMachine Learning for Big Data Analysis

This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large

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 : 25 June 2021
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 Approach for Cloud Data Analytics in IoT

Machine Learning Approach for Cloud Data Analytics in IoT
  • Author : Sachi Nandan Mohanty,Jyotir Moy Chatterjee,Monika Mangla,Suneeta Satpathy,Sirisha Potluri
  • Publisher : John Wiley & Sons
  • Release : 14 July 2021
GET THIS BOOKMachine Learning Approach for Cloud Data Analytics in IoT

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT

Big Data Analytics for Cyber-Physical Systems

Big Data Analytics for Cyber-Physical Systems
  • Author : Guido Dartmann,Houbing Song,Anke Schmeink
  • Publisher : Elsevier
  • Release : 15 July 2019
GET THIS BOOKBig Data Analytics for Cyber-Physical Systems

Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the