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Advanced Analytics And Deep Learning Models

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Advanced Analytics And Deep Learning Models

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Author : Archana Mire
language : en
Publisher: John Wiley & Sons
Release Date : 2022-06-01

Advanced Analytics And Deep Learning Models written by Archana Mire and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.

Advanced Analytics and Deep Learning Models The book provides readers with an in-depth understanding of concepts and technologies related to the importance of analytics and deep learning in many useful real-world applications such as e-healthcare, transportation, agriculture, stock market, etc. Advanced analytics is a mixture of machine learning, artificial intelligence, graphs, text mining, data mining, semantic analysis. It is an approach to data analysis. Beyond the traditional business intelligence, it is a semi and autonomous analysis of data by using different techniques and tools. However, deep learning and data analysis both are high centers of data science. Almost all the private and public organizations collect heavy amounts of data, i.e., domain-specific data. Many small/large companies are exploring large amounts of data for existing and future technology. Deep learning is also exploring large amounts of unsupervised data making it beneficial and effective for big data. Deep learning can be used to deal with all kinds of problems and challenges that include collecting unlabeled and uncategorized raw data, extracting complex patterns from a large amount of data, retrieving fast information, tagging data, etc. This book contains 16 chapters on artificial intelligence, machine learning, deep learning, and their uses in many useful sectors like stock market prediction, a recommendation system for better service selection, e-healthcare, telemedicine, transportation. There are also chapters on innovations and future opportunities with fog computing/cloud computing and artificial intelligence. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in healthcare, telemedicine, transportation, and the financial sector. The book will also be a great source for software engineers and advanced students who are beginners in the field of advanced analytics in deep learning.

Advanced Analytics And Deep Learning Models

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Author : Archana Mire
language : en
Publisher: John Wiley & Sons
Release Date : 2022-05-03

Big Data Analytics Methods

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Author : Peter Ghavami
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-12-16

Big Data Analytics Methods written by Peter Ghavami and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-16 with Business & Economics categories.

Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics

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Author : R. Sujatha
language : en
Publisher: CRC Press
Release Date : 2021-09-22

Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics written by R. Sujatha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Technology & Engineering categories.

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.

Advanced Deep Learning Applications In Big Data Analytics

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Author : Bouarara, Hadj Ahmed
language : en
Publisher: IGI Global
Release Date : 2020-10-16

Advanced Deep Learning Applications In Big Data Analytics written by Bouarara, Hadj Ahmed and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Computers categories.

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.

Big Data Analytics Methods

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Author : Peter Ghavami
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-03-06

Big Data Analytics Methods written by Peter Ghavami and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-06 with categories.

Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensemble of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods are covered. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. This book is ideal as a text book for a course or as a reference for data scientists, data engineers, data analysts, Business intelligence practitioners, and business managers. It covers 10 chapters that discuss natural language processing (NLP), data visualization, prediction, optimization, artificial intelligence, regression analysis, cox hazard model and many analytics use case examples with applications in healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services. Big Data Analytics Methods Is a must read for those who wish to gain confidence and knowledge about big data and advanced analytics techniques. Read this book and confidently speak, lead and guide others about machine learning, neural networks, NLP, deep learning, and over 100 other analytics techniques. This book is fun and easy to read. It starts with simple and broad explanation of methods and gradually introduces more technical terms and techniques layer by layer. It finally introduces the underlying mathematical terms for those who want a mathematical foundation of the analytics methods. This book is one of a kind as it provides state of the art in advanced data analytics methods with important best practices to ensure the reader's success in data analytics.

Data Analytics

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Author : Anthony S. Williams
language : en
Publisher: Anthony S. Williams
Release Date :

Data Analytics written by Anthony S. Williams and has been published by Anthony S. Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

A Book Bundle of Data Analytics for Beginners AND Deep Learning with Keras Data Analytics for Beginners: Introduction to Data Analytics Knowing the data generated by your business every day is a key to success in the Data Analytic World that you are competing in. As there is so much data so, the organizations need to collect and store them. The data becomes valuable to businesses when it is analyzed. Prior to the recent rise in analytics, businesses and organizations did not have the capacity to analyze a great deal of data, so a relatively small amount was maintained. In today's data-driven world, anything and everything may have significance, so there has been an attempt to record and keep virtually any data that we have the capacity to collect; and we have a great deal of capacity. There is so much to learn in this bundle about data analytics and I do invite you to grab your copy today and get started! Deep Learning with Keras: Introduction to Deep Learning with Keras This book will introduce you to various deep learning models in Keras, and you will see how different neural networks can be used in real-world examples as well as in various scientific fields. You will explore various Keras algorithms like the simplest linear regression or more complex deep convolutional network. You will get to know what is the difference between supervised and unsupervised deep learning and you will be able to implement various algorithms in Keras by yourself as you follow step-by-step guide in this book. You will explore various applications of deep learning models such as speech recognition systems, natural language processing, and video game development. A whole new world will open in front of you since, by the time you reach the final page of this book, you will be a Keras expert and ready for your deep-learning projects. By downloading this BOOK BUNDLE you will discover... Data Analytics for Beginners: Putting Data Analytics to Work The Rise of Data Analytics Big Data Defined Cluster Analysis Applications of Cluster Analysis Commonly Graphed Information Data Visualization Four Important Features of Data Visualization Software Big Data Impact Envisaged by 2020 Pros and Cons of Big Data Analytics And of course much more! Deep Learning with Keras: 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 Video Game Development Real World Applications And of course much more! Download this BOOK BUNDLE now and SAVE MONEY!!

No Code Data Science

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Author : David Patrishkoff
language : en
Publisher:
Release Date : 2023-10-16

No Code Data Science written by David Patrishkoff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-16 with Computers categories.

No Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, machine learning, and AI without using a programming language, such as Python or R.It's a comprehensive guide to no-code data science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models. The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters.Data science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on machine learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial intelligence is featured in chapters on image analysis and text mining. Lastly, the potential impact of machine learning and artificial intelligence on Industry 4.0 is covered in the last chapter. A pathway for statisticians, Lean Six Sigma practitioners, and other professionals is offered to learn predictive modeling techniques to enable organizations to successfully pursue Industry 4.0 goals.

Data Analytics

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Author : Anthony S. Williams
language : en
Publisher: Anthony S. Williams
Release Date :

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!!

Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges

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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2020-12-14

Machine Learning And Big Data Analytics Paradigms Analysis Applications And Challenges written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-14 with Computers categories.

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 Analytics And Deep Learning Models

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