2,317 results on 'LN cat08778a'
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2. Clean Ruby: A Guide to Crafting Better Code for Rubyists#
- Author
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Carleton DiLeo
- Subjects
Internet of things ,Machine learning ,Electronic books - Published
- 2020
3. Hands-on mathematics for deep learning : build a solid mathematical foundation for training efficient deep neural networks.
- Author
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Dawani, Jay
- Subjects
Machine learning -- Mathematics - Published
- 2020
4. Hands-On Python Natural Language Processing. [electronic resource]
- Author
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Kedia, Aman, Rasu, Mayank, and Safari, an O'Reilly Media Company.
- Subjects
Electronic books - Abstract
Summary: Get well-versed with traditional as well as modern natural language processing concepts and techniques Key Features Perform various NLP tasks to build linguistic applications using Python libraries Understand, analyze, and generate text to provide accurate results Interpret human language using various NLP concepts, methodologies, and tools Book Description Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding. This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own. By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP. What you will learn Understand how NLP powers modern applications Explore key NLP techniques to build your natural language vocabulary Transform text data into mathematical data structures and learn how to improve text mining models Discover how various neural network architectures work with natural language data Get the hang of building sophisticated text processing models using machine learning and deep learning Check out state-of-the-art architectures that have revolutionized research in the NLP domain Who this book is for This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough...
- Published
- 2020
5. Pattern recognition and computational intelligence techniques using Matlab.
- Author
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Gopi, E. S.
- Subjects
Pattern recognition systems ,Computer vision ,Computational intelligence ,Electronic books - Abstract
Summary: This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way, using Matlab to illustrate topics and concepts. The author covers computational intelligence tools like particle swarm optimization, bacterial foraging, simulated annealing, genetic algorithm, and artificial neural networks. The Matlab based illustrations along with the code are given for every topic. Readers get a quick basic understanding of various pattern recognition techniques using only the required depth in math. The Matlab program and algorithm are given along with the running text, providing clarity and usefulness of the various techniques. Presents pattern recognition and the computational intelligence using Matlab; Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly; Outlines an array of classifiers, various regression models, statistical tests and the techniques for pattern recognition using computational intelligence.
- Published
- 2020
6. Reinventing clinical decision support : data analytics, artificial intelligence, and diagnostic reasoning.
- Author
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Cerrato, Paul and Halamka, John D.
- Subjects
Diagnosis -- Decision making -- Data processing ,Clinical medicine -- Decision making -- Data processing ,Expert systems (Computer science) ,BUSINESS & ECONOMICS -- Industries -- Service Industries ,COMPUTERS -- Information Technology ,COMPUTERS -- Programming -- Systems Analysis & Design ,Electronic books - Abstract
Summary: This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it's forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.
- Published
- 2020
7. Data journalism in the global south.
- Author
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Mutsvairo, Bruce, Bebawi, Saba, and Borges-Rey, Eddy
- Subjects
Journalism -- Developing countries -- Data processing ,Journalism -- Data processing ,Electronic books - Abstract
Summary: This volume seeks to analyse the emerging wave of data journalism in the Global South. It does so by examining trends, developments and opportunities for data journalism in the aforementioned contexts. Whilst studies in this specific form of journalism are increasing in numbers and significance, there remains a dearth of literature on data journalism in less developed regions of the world. By demonstrating an interest in data journalism across countries including Chile, Argentina, the Philippines, South Africa and Iran, among others, this volume contributes to multifaceted transnational debates on journalism, and is a crucial reference text for anyone interested in data journalism in the 'developing' world. Drawing on a range of voices from different fields and nations, sharing empirical and theoretical experiences, the volume aims to initiate a global dialogue among journalism practitioners, researchers and students.
- Published
- 2019
8. Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets.
- Author
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Ciaburro, Giuseppe and Joshi, Prateek
- Subjects
Python (Computer program language) ,Machine learning ,Electronic books - Published
- 2019
9. Behavior trees in robotics and Al : an introduction.
- Author
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Colledanchise, Michele, Ögren, Petter, and Taylor and Francis.
- Subjects
Decision trees ,Robots -- Control systems - Abstract
Abstract: Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality.? BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs.This book introduces the subject of BTs from simple topics, such as semantics and design principles, to complex topics, such as learning and task planning. For each topic, the authors provide a set of examples, ranging from simple illustrations to realistic complex behaviors, to enable the reader to successfully combine theory with practice.Starting with an introduction to BTs, the book then describes how BTs relate to, and in many cases, generalize earlier switching structures, or control architectures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. The book then presents a set of important extensions and provides a set of tools for formally analyzing these extensions using a state space formulation of BTs. With the new analysis tools, the book then formalizes the descriptions of how BTs generalize earlier approaches and shows how BTs can be automatically generated using planning and learning. The final part of the book provides an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.This book targets a broad audience, including both students and professionals interested in modeling complex behaviors for robots, game characters, or other AI agents. Readers can choose at which depth and pace they want to learn the subject, depending on their needs and background.
- Published
- 2018
10. Business analytics.
- Author
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Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
11. Business analytics.
- Author
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Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
12. Hands-on supervised machine learning with Python. [electronic resource]
- Subjects
Python (Computer program language) ,Machine learning ,Instructional films - Abstract
Summary: Teach your machine to think for itself! About This Video: Take a deep dive into supervised learning and grasp how a machine "learns" from data. Follow detailed and thorough coding examples to implement popular machine learning algorithms from scratch, developing a deep understanding along the way. Work your Python muscle! This course will help you grow as a developer by heavily relying on some of the most popular scientific and mathematical libraries in the Python language. In Detail: Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it's here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine "learns" under the hood.This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning. Next, we'll explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning. By the end of the video course, you'll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems. All the codes of the course are uploaded on GitHub.
- Published
- 2018
13. Large-Scale Machine Learning in the Earth Science.
- Author
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Srivastava, Ashok N. (Ashok Narain), 1969-, (editor.)
- Subjects
Electronic books - Published
- 2018
14. Advances in Soft Computing and Machine Learning in Image Processing. [electronic resource].
- Author
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Hassanien, Aboul Ella and Oliva, Diego Alberto
- Subjects
Electronic books - Published
- 2017
15. Designing voice user interfaces : principles of conversational experiences.
- Author
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Pearl, Cathy
- Subjects
User interfaces (Computer systems) ,Human-computer interaction ,User-centered system design ,Automatic speech recognition ,Electronic books - Abstract
Summary: "Voice user interfaces (VUIs) are becoming all the rage today. But how do you build one that people can actually converse with? Whether you're designing a mobile app, a toy, or a device such as a home assistant, this practical book guides you through basic VUI design principles, helps you choose the right speech recognition engine, and shows you how to measure your VUI's performance and improve upon it"--Back cover.
- Published
- 2017
16. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
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Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks (Computer science) ,Deep learning ,Künstliche Intelligenz ,Maschinelles Lernen ,Electronic books - Published
- 2017
17. Mobile Big Data : a Roadmap from Models to Technologies.
- Author
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Skourletopoulos, Georgios, Mastorakis, George, Mavromoustakis, Constandinos X., Dobre, Ciprian, and Pallis, Evangelos
- Subjects
Electronic books - Published
- 2017
18. Open data and the knowledge society.
- Author
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Wessels, Bridgette, Finn, Rachel L., Wadhwa, Kush, and Sveinsdottir, Thordis
- Subjects
Information society -- Social aspects ,Informationsgesellschaft ,Open Data ,05.20 communication and society ,Media Studies - Abstract
While there is a lot of talk about how we now live in a knowledge society, the reality has been less impressive: We have yet to truly transition to a knowledge society - in part, this book argues, because discussion mostly focuses on a knowledge economy and information society rather than on ways to mobilise to create an actual knowledge society. That all may change, however, with the rise of open data and big data. This book considers the role of the open data movement in fostering transformation, showing that at the heart of any successful mobilisation will be an emerging open data ecosystem and new ways for societal actors to effectively produce and use data.
- Published
- 2017
19. Practical statistics for data scientists : 50 essential concepts.
- Author
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Bruce, Peter C., Bruce, Andrew, and ProQuest (Firm)
- Subjects
Mathematical analysis -- Statistical methods ,Quantitative research -- Statistical methods ,Big data -- Mathematics ,REFERENCE -- Questions & Answers ,Statistics ,Statistics -- Data processing ,Data Mining ,Datenanalyse ,Statistik ,Electronic books - Published
- 2017
20. A First Course in Machine Learning.
- Author
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Rogers, Simon and Girolami, Mark
- Subjects
Machine learning ,COMPUTERS -- General ,Data Mining ,Maschinelles Lernen ,Machine Learning ,Electronic books - Published
- 2016
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