learning from data - 三民網路書店

Fundamentals of Python: From First Programs Through Data Structures

作者：Kenneth Alfred Lambert; Martin Osborne (CON) 出版社：Cengage Learning 出版日：2009/01/07 裝訂：平裝

In FUNDAMENTALS OF PYTHON: FROM FIRST PROGRAMS THROUGH DATA STRUCTURES, Washington and Lee University professor Kenneth A. Lambert presents all of the important topics in CS1 and CS2 in one volume. Th

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Obtaining Value from Big Data for Service Delivery

90折

122.

Obtaining Value from Big Data for Service Delivery

出版社：Cengage Learning 出版日：2015/12/19 裝訂：平裝

Big data is an emerging phenomenon that has enormous implications and impacts upon business strategy, profitability, and process improvements. All service systems generate big data these days, especia

定價：2518 元，優惠價：9 2266

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Applied Machine Learning for Data Scientists and Software Engineers ─ Framing - the First Steps Toward Successful Execution

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123.

Applied Machine Learning for Data Scientists and Software Engineers ─ Framing - the First Steps Toward Successful Execution

作者：Andrew Kelleher; Adam Kelleher 出版社：Addison-Wesley Professional 出版日：2017/09/18 裝訂：平裝

The typical data science task in industry starts with an “ask” from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the contex

定價：2250 元，優惠價：1 2250

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Exam Ref 70-774 Perform Cloud Data Science With Azure Machine Learning

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124.

Exam Ref 70-774 Perform Cloud Data Science With Azure Machine Learning

作者：Paco Gonzalez 出版社：Microsoft Pr 出版日：2018/01/06 裝訂：平裝

Direct from Microsoft, this Exam Ref is the official study guide for the Microsoft 70-774 Perform Cloud Data Science with Azure Machine Learning certification exam, the second of two exams required fo

定價：1800 元，優惠價：1 1800

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Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

79折

125.

Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

作者：John D. Kelleher; Brian MAC Namee; Aoife D'arcy 出版社：Mit Pr 出版日：2015/07/24 裝訂：精裝

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 a

定價：3200 元，優惠價：79 2528

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126.

Veracity of Big Data ― Machine Learning and Other Approaches to Verifying Truthfulness

作者：Vishnu Pendyala 出版社：Apress 出版日：2018/07/31 裝訂：平裝

Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V’s of big data, including veracity, and study the problem from various angles. The sol

定價：1485 元，優惠價：1 1485

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127.

Materials Discovery and Design ― By Means of Data Science and Optimal Learning

作者：Turab Lookman (EDT); Stephan Eidenbenz (EDT); Frank Alexander (EDT); Cris Barnes (EDT) 出版社：Springer Nature 出版日：2018/10/18 裝訂：精裝

This book addresses the current status, challenges and future directions of data-driven materials discovery and design. It presents the analysis and learning from data as a key theme in many science a

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128.

European Marketing Data & Statistics 2014

作者：Euromonitor (COR) 出版社：Gale ; Cengage Learning 出版日：2014/02/15 裝訂：精裝

EUROPEAN MARKETING DATA AND STATISTICS 2013 presents comparable demographic, economic and marketing statistics for 44 European countries. Statistics researched range from advertising, health, media an

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Data, Data Everywhere ─ Bringing All the Data Together for Continuous School Improvement

90折

129.

Data, Data Everywhere ─ Bringing All the Data Together for Continuous School Improvement

作者：Victoria Bernhardt 出版社：Taylor & Francis 出版日：2015/08/24 裝訂：平裝

The latest book from best-selling author Victoria L. Bernhardt is an easy-to-read primer that describes what it takes to achieve student learning growth at every grade level, in every subject area, an

定價：2064 元，優惠價：9 1858

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Machine Learning for Algorithmic Trading：Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

95折

130.

Machine Learning for Algorithmic Trading：Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

作者：Stefan Jansen 出版社：Packt Publishing Limited 出版日：2020/07/31 裝訂：平裝

定價：3079 元，優惠價：95 2925

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Learning to Love Data Science ─ Explorations of Emerging Technologies and Platforms for Predictive Analytics, Machine Learning, Digital Manufacturing, and Supply Chain Optimization

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131.

Learning to Love Data Science ─ Explorations of Emerging Technologies and Platforms for Predictive Analytics, Machine Learning, Digital Manufacturing, and Supply Chain Optimization

作者：Mike Barlow 出版社：Oreilly & Associates Inc 出版日：2015/10/25 裝訂：平裝

From IT to advanced analtyics, and from data for social good to data for predictive maintenance, this anthology of reports gives a broad overview of the data space—including the applications that have

定價：875 元，優惠價：1 875

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132.

Machine Learning for Beginners

作者：David Brown 出版社：Independently published 出版日：2019/12/10 裝訂：平裝

Learn Machine Learning, Deep Learning, Data Science and More!Machine learning is here; it is changing the world in ways you might not know yet. From search engines to speech recognition on your phone,

定價：798 元，優惠價：1 798

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133.

Introduction to Machine Learning

作者：Etienne Bernard 出版社：Wolfram Media Inc 出版日：2021/12/20 裝訂：平裝

Machine learning-a computer's ability to learn-is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well.Introduction to Machine Learning weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kep

定價：1573 元，優惠價：1 1573

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134.

Personalized Machine Learning

作者：Julian McAuley 出版社：Cambridge Univ Pr 出版日：2022/01/31 裝訂：精裝

Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design mod

定價：2599 元，優惠價：9 2339

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90折

135.

Learning to Talk

作者：John McShane 出版社：Cambridge Univ Pr 出版日：2010/03/18 裝訂：平裝

How do children learn to talk? This fundamental question continues to be a subject of lively and contentious debate among linguists and psychologists. Originally published in 1980, Dr McShane's discerning analysis of the theoretical issues involved takes account of the contribution of speech-act theory and of Gricean meaning theory to our understanding of communication. The wide-ranging discussion of the work of other researchers provides the reader with a clear perspective in which to assess Dr McShane's own approach. Dr McShane shows that limited, but effective, communication is possible in the absence of words, and then traces the genesis of communication through the one-word stage to the development of grammatically structured utterances. His arguments are supported by data from the longitudinal study he carried out with six children between the ages of one and two years. This study and its implications were important for all those professionally interested in language development.

定價：1559 元，優惠價：9 1403

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136.

Prediction, Learning, and Games

作者：Nicolo Cesa-Bianchi 出版社：Cambridge Univ Pr 出版日：2006/03/13 裝訂：精裝

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that o

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137.

Scaling Up Machine Learning

作者：Ron Bekkerman 出版社：Cambridge Univ Pr 出版日：2011/12/30 裝訂：精裝

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algo

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90折

138.

Learning Analytics Explained

作者：Sclater 出版社：Routledge UK 出版日：2017/02/28 裝訂：平裝

The increasing use of educational technology is enabling the collection of massive amounts of data from a wide variety of sources about students and their online activities. Introduction to Learning A

定價：2404 元，優惠價：9 2164

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139.

Learning Analytics Explained

作者：Sclater 出版社：Routledge UK 出版日：2017/02/28 裝訂：精裝

The increasing use of educational technology is enabling the collection of massive amounts of data from a wide variety of sources about students and their online activities. Introduction to Learning A

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140.

Deep Learning on Graphs

作者：Yao Ma 出版社：Cambridge Univ Pr 出版日：2021/08/31 裝訂：精裝

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

定價：2924 元，優惠價：9 2632

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Fundamentals of Python: From First Programs Through Data Structures

Obtaining Value from Big Data for Service Delivery

Applied Machine Learning for Data Scientists and Software Engineers ─ Framing - the First Steps Toward Successful Execution

Exam Ref 70-774 Perform Cloud Data Science With Azure Machine Learning

Fundamentals of Machine Learning for Predictive Data Analytics ─ Algorithms, Worked Examples, and Case Studies

Veracity of Big Data ― Machine Learning and Other Approaches to Verifying Truthfulness

Materials Discovery and Design ― By Means of Data Science and Optimal Learning

European Marketing Data & Statistics 2014

Data, Data Everywhere ─ Bringing All the Data Together for Continuous School Improvement

Machine Learning for Algorithmic Trading：Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition

Learning to Love Data Science ─ Explorations of Emerging Technologies and Platforms for Predictive Analytics, Machine Learning, Digital Manufacturing, and Supply Chain Optimization

Machine Learning for Beginners

Introduction to Machine Learning

Personalized Machine Learning

Learning to Talk

Prediction, Learning, and Games

Scaling Up Machine Learning

Learning Analytics Explained

Learning Analytics Explained

Deep Learning on Graphs