Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics, Sujatha R., Aarthy S. L., Vettriselvan R.
Автор: Koushik Ghosh, Souvik Bhattacharyya Название: Noise Filtering for Big Data Analytics ISBN: 3110697092 ISBN-13(EAN): 9783110697094 Издательство: Walter de Gruyter Рейтинг: Цена: 173490.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book explains how to perform data de-noising, in large scale, with a satisfactory level of accuracy. Three main issues are considered. Firstly, how to eliminate the error propagation from one stage to next stages while developing a filtered model.
Secondly, how to maintain the positional importance of data whilst purifying it. Finally, preservation of memory in the data is crucial to extract smart data from noisy big data. If, after the application of any form of smoothing or filtering, the memory of the corresponding data changes heavily, then the final data may lose some important information.
This may lead to wrong or erroneous conclusions. But, when anticipating any loss of information due to smoothing or filtering, one cannot avoid the process of denoising as on the other hand any kind of analysis of big data in the presence of noise can be misleading. So, the entire process demands very careful execution with efficient and smart models in order to effectively deal with it.
Автор: Hassanien Aboul Ella, Darwish Ashraf Название: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges ISBN: 3030593371 ISBN-13(EAN): 9783030593377 Издательство: Springer Цена: 186330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Today 91% of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence, so future programmers will no longer find work without having knowledge of this particular field that is constantly evolving. Artificial intelligence has made great strides over the years, and by 2024 it is estimated that 77% of programmers will have to be experts in this field to implement it in the various programming languages. So if you are such a programmer or aspirant and ignore the importance that Python data analysis and artificial intelligence have in our future, then you will be cut off from the business world. The solution? You need to learn these things and, above all, do it in a clear, simple, and practical way. The goal of Python for Data Science is to give you an advanced level training on Python, artificial intelligence, and deep machine learning as quickly as possible.What are some points you will learn in this book?- Artificial Intelligence: How Does it Work? How is it Used?- The Key Elements of Machine Learning- Machine Learning vs Deep Learning- Data Science vs Business Intelligence- The Data Science Lifecycle- The Value of Big Data Explained to a Child- 4 Tips for Data Cleaning and Organizing Your Data- Python Data Analysis 360 - 6 Different Machine Learning Algorithms- How to Handle Data VisualizationsPython for Data Science is perfect for those who already look to the future and want to ensure a job for the next 20 years by beating the competition, even if you know nothing about computer codes and you have never turned on a computer in your life. Would You Like to Know More? Buy now to find out about Python for Data Science.
Today 91% of Python programmers are not quite prepared. This is what the IT companies say, according to a recent survey. Do you know why? The reason is that they have no notions of artificial intelligence, so future programmers will no longer find work without having knowledge of this particular field that is constantly evolving. Artificial intelligence has made great strides over the years, and by 2024 it is estimated that 77% of programmers will have to be experts in this field to implement it in the various programming languages. So if you are such a programmer or aspirant and ignore the importance that Python data analysis and artificial intelligence have in our future, then you will be cut off from the business world. The solution? You need to learn these things and, above all, do it in a clear, simple, and practical way. The goal of Python for Data Science is to give you an advanced level training on Python, artificial intelligence, and deep machine learning as quickly as possible.What are some points you will learn in this book?- Artificial Intelligence: How Does it Work? How is it Used?- The Key Elements of Machine Learning- Machine Learning vs Deep Learning- Data Science vs Business Intelligence- The Data Science Lifecycle- The Value of Big Data Explained to a Child- 4 Tips for Data Cleaning and Organizing Your Data- Python Data Analysis 360 - 6 Different Machine Learning Algorithms- How to Handle Data VisualizationsPython for Data Science is perfect for those who already look to the future and want to ensure a job for the next 20 years by beating the competition, even if you know nothing about computer codes and you have never turned on a computer in your life. Would You Like to Know More? Buy now to find out about Python for Data Science.
Автор: Fong, Simon James, Millham, Richard C Название: Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing ISBN: 9811566941 ISBN-13(EAN): 9789811566943 Издательство: Springer Рейтинг: Цена: 158380.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book aims to provide some insights into recently developed bio-inspired algorithms within recent emerging trends of fog computing, sentiment analysis, and data streaming as well as to provide a more comprehensive approach to the big data management from pre-processing to analytics to visualization phases.
Автор: Murad Khan; Bilal Jan; Haleem Farman Название: Deep Learning: Convergence to Big Data Analytics ISBN: 9811334587 ISBN-13(EAN): 9789811334580 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Поставка под заказ. Описание:
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.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Pandian Vasant Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 179981193X ISBN-13(EAN): 9781799811930 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 180180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: J. Joshua Thomas, Pinar Karagoz, B. Bazeer Ahamed, Название: Deep Learning Techniques and Optimization Strategies in Big Data Analytics ISBN: 1799811921 ISBN-13(EAN): 9781799811923 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 239310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: Rajshree Srivastava, Pradeep Kumar Mallick, Siddha Название: Computational Intelligence for Machine Learning and Healthcare Informatics ISBN: 3110647826 ISBN-13(EAN): 9783110647822 Издательство: Walter de Gruyter Цена: 136310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Автор: Iman Rahimi, Amir H. Gandomi, Simon James Fong Название: Big data analytics in supply chain management ISBN: 0367407175 ISBN-13(EAN): 9780367407179 Издательство: Taylor&Francis Рейтинг: Цена: 168430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book discusses the results of a recent large-scale achievement on Big Data Analytics (BDA) topics among Supply Chain Management (SCM) professionals The book intends to show a diversity of supply chain management issues that may benefit from BDA, both in theory and practice.
Автор: Nokeri Tshepo Chris Название: Web App Development and Real-Time Web Analytics with Python: Develop and Integrate Machine Learning Algorithms into Web Apps ISBN: 1484277821 ISBN-13(EAN): 9781484277829 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Static 2D and 3D GraphsChapter Goal: This chapter introduces the basics of tabulating data and constructing staticgraphical representations. To begin with, it exhibits an approach of extracting and tabulating data by implementing the pandas and sqlalchemy library. Subsequently, it reveals a prevalent 2D and 3D charting recognized as Matplotlib, then exhibits a technique of constructing basic charts (i.e. box-whisker plot, histogram, line plot, and scatter plot).● Tabulating Data● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Plot● 3D Charting● Conclusion Chapter 2: Interactive ChartingChapter Goal: This chapter introduces an approach for constructing interactive charts byimplementing the most prevalent library, recognized as Plotly.● Plotly● 2D Chartingo Box-whisker-ploto Histogramo Line ploto Scatter ploto Density Ploto Bar Charto Pie Charto Sunburst● 3D Charting● Conclusion Chapter 3: Containing functionality in Interactive GraphsChapter Goal: This chapter extends to the preceding chapter. It introduces an approach toupdating interactive graphs to improve user experience. For instance, you will learn how to add buttons and range sliders, among other functionalities. Besides that, it exhibits an approach for integrating innumerable graphs into one graph with some functionality.● Updating Graph Layout● Updating Plotly Axes● Including Range Slider● Including Buttons to a Graph● Styling Interactive Graphs● Updating Plotly X-Axis● Color Sequencing● Subplots● Conclusions Chapter 4: Essentials of HTMLChapter Goal: This chapter introduces the most prevalent markup language for developingwebsites. It acquaints you with the essentials of designing websites. Besides that, it contains a richset of code and examples to support you in getting started with coding using HTML.● The Communication between a Web Browser and Web Server● Domain Hostingo Shared Hostingo Managed Hosting● HyperText Markup Languageo HTML Elements▪ Headings▪ Paragraphs▪ Div▪ Span▪ Buttons▪ Text Box▪ Input▪ File Upload▪ Label▪ Form▪ Meta Tag● Practical Example● Conclusion Chapter 5: Python Web Frameworks and ApplicationsChapter Goal: The preceding chapter acquainted you with interactive visualization using Plotly. This chapter introduces key Python web frameworks (i.e., flask and dash) and how they differ.Besides that, it provides practical examples and helps you get started with Python web development.● Web Frameworks● Web Applications● Flasko WSGIo Werkzeugo Jinjao Installing Flasko Initializing a Flask Web Applicationo Flask Application Codeo Deploy a Flask Web Application● Dasho Installing Dash Dependencieso Initializing a Dash Web Applicationo Dash Application Codeo Deploy a Dash Web Application● Jupyter Dash● Conclusion Chapter 6: Dash Bootstrap ComponentsChapter Goal: This chapter covers dash_bootstrap_component. It is a Python library from the Plotly family, which enables us to have key bootstrap func
Автор: Emrouznejad Название: Big Data Optimization: Recent Developments and Challenges ISBN: 3319302639 ISBN-13(EAN): 9783319302638 Издательство: Springer Рейтинг: Цена: 139310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Themain objective of this book is to provide the necessary background to work withbig data by introducing some novel optimization algorithms and codes capable ofworking in the big data setting as well as introducing some applications in bigdata optimization for both academics and practitioners interested, and tobenefit society, industry, academia, and government. Presenting applications ina variety of industries, this book will be useful for the researchers aiming toanalyses large scale data. Several optimization algorithms for big dataincluding convergent parallel algorithms, limited memory bundle algorithm,diagonal bundle method, convergent parallel algorithms, network analytics, andmany more have been explored in this book.
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