Natural Language Annotation for Machine Learning

Natural Language Semantics discusses fundamental concepts for linguistic semantics. This book combines theoretical explanations of several methods of inquiry with detailed semantic analysis and emphas

Natural Language Semantics discusses fundamental concepts for linguistic semantics. This book combines theoretical explanations of several methods of inquiry with detailed semantic analysis and emphas

This second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. It

These contributions converge on an intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theor

This book introduces the newly developed Extreme Learning Machine (ELM) including its theories and learning algorithms. ELM is a unified framework of broad type of generalized single-hidden layer feed

Data-Driven Learning (DDL) can be broadly defined as the use of corpus tools and techniques for learners and teachers of foreign or second language, typically in the form of concordances derived from authentic texts for inductive learning of lexicogrammar. This Element is a practical guide for language teachers and graduate students intending to explore or upgrade their use of corpora in the language classroom and beyond. In today's context, where advances in computing and information processing dominate our social and professional interactions, the use of corpora emerges as a prime resource with which to approach data-driven language learning and teaching, developing language awareness, noticing skills and critical thinking for learning that generative AI cannot do for you.

Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on such topics as GANs, transformers, BERT, GPT, VAE, adversarial examples, pre-training and fine-tuning, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new

Migrate from pandas and scikit-Learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Na鴳e Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using

Due to the rapid development of gaming technologies in recent years, there has been a surge of interest in the role that digital games can play in foreign and second language learning. Bringing together innovative research from an international team of contributors, this book provides a comprehensive overview of the use of digital games in computer-assisted language learning (CALL).The book firstly lays the theoretical foundations and outlines various rationales for using digital games, incorporating contemporary theories of second language acquisition. It also explores the development and impact of digital games designed specifically for language learning, giving due consideration to design principles, pedagogical requirements and student health. Chapters then draw on case studies from Europe and Japan to analyse in-game interaction, attitudes and participation in both institutional and out-of-classroom settings. Seamlessly combining theory with practical application, this book outlin

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, data science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from machine learning and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and machine learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

In Deep Learning with Python, Second Edition, updated from the original bestseller with over 50% new content, you'll explore challenging concepts and practice applications in computer vision, natural-language processing, and generative models. The bestseller revised Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library, written by the creator of Keras himself. This revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. In Deep Learning with Python, Second Edition, updated from the original bestseller with over 50% new content, you'll explore challenging concepts and practice applications in computer vision, natural-language processing, and generative models, building your understanding through practical examples and intuitive explanations that make the complexities of deep learning easily accessible. Purchase of the print book

Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description: Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with

Learning language and using language to learn is at the core of any educational activity. Bringing together a globally representative team of experts, this volume presents an innovative and empirically robust collection of studies that examine the role of language in education, with a particular emphasis on features of school-relevant language in middle childhood and adolescents, and its precursors in early childhood. It addresses issues such as how children's linguistic and literacy experiences at home prepare them for school, how the classroom functions as a language-mediated learning environment, and how schools can support language minority students in academic attainment. Set in three parts - Early Childhood, Middle Childhood and Adolescence and Learning in Multilingual Contexts - each part features a discussion from experts in the field to stimulate conversation and further routes for research. Its structure will make it useful for anyone interested in ongoing efforts towards bui

Providing a comprehensive survey of cutting-edge work on second language learning, this Handbook, written by a team of leading experts, surveys the nature of second language learning and its implications for teaching. Prominent theories and methods from linguistics, psycholinguistics, processing-based, and cognitive approaches are covered and organised thematically across sections dealing with skill development, individual differences, pedagogical interventions and approaches, and context and environment. This state-of-the-art volume will interest researchers in second language studies and language education, and will also reach out to advanced undergraduate and graduate students in these and other related areas.

Originally published in 1992, this title reviews seven major subareas in artificial intelligence at that time: knowledge acquisition; logic programming and representation; machine learning; natural la

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you've been curious about machine learning but didn't k

An introduction to natural language processing with Python using spaCy, a leading Python natural language processing library.Natural Language Processing with Python and spaCy will show you how to crea

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.

This book is an advanced introduction to semantics that presents this crucial component of human language through the lens of the 'Meaning-Text' theory - an approach that treats linguistic knowledge as a huge inventory of correspondences between thought and speech. Formally, semantics is viewed as an organized set of rules that connect a representation of meaning (Semantic Representation) to a representation of the sentence (Deep-Syntactic Representation). The approach is particularly interesting for computer assisted language learning, natural language processing and computational lexicography, as our linguistic rules easily lend themselves to formalization and computer applications. The model combines abstract theoretical constructions with numerous linguistic descriptions, as well as multiple practice exercises that provide a solid hands-on approach to learning how to describe natural language semantics.

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for ad

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

Providing a comprehensive survey of cutting-edge work on second language learning, this Handbook, written by a team of leading experts, surveys the nature of second language learning and its implications for teaching. Prominent theories and methods from linguistics, psycholinguistics, processing-based, and cognitive approaches are covered and organised thematically across sections dealing with skill development, individual differences, pedagogical interventions and approaches, and context and environment. This state-of-the-art volume will interest researchers in second language studies and language education, and will also reach out to advanced undergraduate and graduate students in these and other related areas.

Learning language and using language to learn is at the core of any educational activity. Bringing together a globally representative team of experts, this volume presents an innovative and empirically robust collection of studies that examine the role of language in education, with a particular emphasis on features of school-relevant language in middle childhood and adolescents, and its precursors in early childhood. It addresses issues such as how children's linguistic and literacy experiences at home prepare them for school, how the classroom functions as a language-mediated learning environment, and how schools can support language minority students in academic attainment. Set in three parts - Early Childhood, Middle Childhood and Adolescence and Learning in Multilingual Contexts - each part features a discussion from experts in the field to stimulate conversation and further routes for research. Its structure will make it useful for anyone interested in ongoing efforts towards bui

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentati

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies from each of these domains: you’ll see examples that demonst

Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use i

Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cyc

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learnKey FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machin

Developing Early Communication, Language and Learning Skills at Home is full of tried-and-tested, practical activities for children developing their communication, language and learning skills includi

Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identificati

Just about anyone with the slightest bit of interest in modern technology is looking to learn more about Machine Learning. This innovative new form of computer programming is the primary tool that mak

This book opens up new lines of debate in language learning and intercultural communication through an investigation of tandem language learning (a method of language learning based on mutual language

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine

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