Data Science Exploration In Artificial Intelligence
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An Intuitive Exploration Of Artificial Intelligence
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Author : Simant Dube
language : en
Publisher: Springer Nature
Release Date : 2021-06-21
An Intuitive Exploration Of Artificial Intelligence written by Simant Dube and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-21 with Computers categories.
This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future. An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential. The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.
Data Analytics And Ai
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Author : Jay Liebowitz
language : en
Publisher: CRC Press
Release Date : 2020-08-06
Data Analytics And Ai written by Jay Liebowitz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-06 with Computers categories.
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 they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.
Encyclopedia Of Data Science And Machine Learning
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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20
Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Artificial Intelligence And Data Science For Beginners
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Author : Bar, Ashton
language : en
Publisher: Ashton Bar
Release Date :
Artificial Intelligence And Data Science For Beginners written by Bar, Ashton and has been published by Ashton Bar this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Skip the lengthy textbook and learn the fundamentals behind Artificial Intelligence and Data Science in this book. This manual is designed to provide a concise yet comprehensive overview of the key concepts behind these fields and their intersection. If you're a beginner looking to get started, this guide will equip you with the essential knowledge needed to understand and navigate the world of AI and data science. You will even learn basic applied mathematical methods, SQL programming, and Python programming to get you started.
Artificial Intelligence And Data Analytics For Energy Exploration And Production
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Author : Fred Aminzadeh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-09-21
Artificial Intelligence And Data Analytics For Energy Exploration And Production written by Fred Aminzadeh 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 2022-09-21 with Science categories.
ARTIFICAL INTELLIGENCE AND DATA ANALYTICS FOR ENERGY EXPLORATION AND PRODUCTION This groundbreaking new book is written by some of the foremost authorities on the application of data science and artificial intelligence techniques in exploration and production in the energy industry, covering the most comprehensive and updated new processes, concepts, and practical applications in the field. The book provides an in-depth treatment of the foundations of Artificial Intelligence (AI) Machine Learning, and Data Analytics (DA). It also includes many of AI-DA applications in oil and gas reservoirs exploration, development, and production. The book covers the basic technical details on many tools used in “smart oil fields”. This includes topics such as pattern recognition, neural networks, fuzzy logic, evolutionary computing, expert systems, artificial intelligence machine learning, human-computer interface, natural language processing, data analytics and next-generation visualization. While theoretical details will be kept to the minimum, these topics are introduced from oil and gas applications viewpoints. In this volume, many case histories from the recent applications of intelligent data to a number of different oil and gas problems are highlighted. The applications cover a wide spectrum of practical problems from exploration to drilling and field development to production optimization, artificial lift, and secondary recovery. Also, the authors demonstrate the effectiveness of intelligent data analysis methods in dealing with many oil and gas problems requiring combining machine and human intelligence as well as dealing with linguistic and imprecise data and rules.
Artificial Intelligence And Data Science For Sustainability Applications And Methods
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Author : Syafrudin, Muhammad
language : en
Publisher: IGI Global
Release Date : 2025-02-07
Artificial Intelligence And Data Science For Sustainability Applications And Methods written by Syafrudin, Muhammad and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-07 with Computers categories.
The integration of artificial intelligence (AI) and data science (DS) into sustainability efforts is increasingly crucial as the world faces environmental and resource challenges. By leveraging advanced AI and DS techniques, we can create innovative solutions for sectors like energy, agriculture, and waste management, promoting long-term sustainability. These technologies have the potential to optimize processes, reduce environmental footprints, and support smarter decision-making across industries. Ultimately, AI and DS are key to advancing both the efficiency and impact of sustainability efforts, contributing to a more sustainable future for society at large. Artificial Intelligence and Data Science for Sustainability: Applications and Methods explores the application of AI and DS techniques and methodologies in addressing various sustainability challenges. It brings together cutting-edge research, case studies, and practical applications that demonstrate how AI can be leveraged to promote sustainable development across diverse domains. Covering topics such as structural optimization, drone imagery, and development financing, this book is an excellent resource for computer scientists, computer engineers, urban planners, sustainability professionals, government officials, policymakers, researchers, professionals, scholars, academicians, and more.
Pandas Cookbook
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Author : Theodore Petrou
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-10-23
Pandas Cookbook written by Theodore Petrou and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-23 with Computers categories.
Over 95 hands-on recipes to leverage the power of pandas for efficient scientific computation and data analysis About This Book Use the power of pandas to solve most complex scientific computing problems with ease Leverage fast, robust data structures in pandas to gain useful insights from your data Practical, easy to implement recipes for quick solutions to common problems in data using pandas Who This Book Is For This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory. What You Will Learn Master the fundamentals of pandas to quickly begin exploring any dataset Isolate any subset of data by properly selecting and querying the data Split data into independent groups before applying aggregations and transformations to each group Restructure data into tidy form to make data analysis and visualization easier Prepare real-world messy datasets for machine learning Combine and merge data from different sources through pandas SQL-like operations Utilize pandas unparalleled time series functionality Create beautiful and insightful visualizations through pandas direct hooks to Matplotlib and Seaborn In Detail This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas library to generate results. Style and approach The author relies on his vast experience teaching pandas in a professional setting to deliver very detailed explanations for each line of code in all of the recipes. All code and dataset explanations exist in Jupyter Notebooks, an excellent interface for exploring data.
Embedding Artificial Intelligence Into Erp Software
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Author : Siar Sarferaz
language : en
Publisher: Springer Nature
Release Date : 2024-05-30
Embedding Artificial Intelligence Into Erp Software written by Siar Sarferaz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-30 with Computers categories.
This book explains how to embed artificial intelligence in digitized business processes of ERP software by solving the two related substantial challenges: how can artificial intelligence be systematically integrated into ERP business processes for ease of consumption, and how can artificial intelligence be made enterprise-ready by covering ERP qualities like compliance, lifecycle management, extensibility, or scalability? As a general introduction, the first part of this book takes the reader through a historical journey towards intelligent ERP systems. In addition, reference processes and a reference architecture for ERP systems are proposed which build the foundation for the suggested subsequent solution concept, including a method for operationalizing intelligence for ERP business processes. Subsequently, in the second part detailed concepts of embedding artificial intelligence into ERP software are proposed. In this context the suggested solution architecture is depicted, and specific topics are resolved like data integration, model validation, explainability, data protection and privacy, model degradation and performance. In the last part an implementation framework is suggested which enables the previously introduced concepts and harmonizes the development and operations of artificial intelligent ERP applications. This part concludes with case studies considering artificial intelligence scenarios of SAP S/4HANA in the areas of logistics, finance and sales which apply the defined solution approach and shows its real-world feasibility. This book is written for professionals who want to implement (as developers) or exploit (as business analysts or consultants) or consider/plan the implementation/exploitation (as managers) of artificial intelligence in business information systems.
Data Science For Cyber Security
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Author : Nicholas A Heard
language : en
Publisher: World Scientific
Release Date : 2018-09-26
Data Science For Cyber Security written by Nicholas A Heard and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-26 with Computers categories.
Cyber-security is a matter of rapidly growing importance in industry and government. This book provides insight into a range of data science techniques for addressing these pressing concerns.The application of statistical and broader data science techniques provides an exciting growth area in the design of cyber defences. Networks of connected devices, such as enterprise computer networks or the wider so-called Internet of Things, are all vulnerable to misuse and attack, and data science methods offer the promise to detect such behaviours from the vast collections of cyber traffic data sources that can be obtained. In many cases, this is achieved through anomaly detection of unusual behaviour against understood statistical models of normality.This volume presents contributed papers from an international conference of the same name held at Imperial College. Experts from the field have provided their latest discoveries and review state of the art technologies.