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Authors: - (1978) - -
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Authors: - (1977) - This book is addressed to researchers and students of the neuropsychology of language, whether they call themselves psychologists, neuropsychologists, neurologists, or linguists.
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Authors: Larsen-Freeman, Diane (1991) - This book provides a synthesis of empirical findings on second and foreign language learning by children and adults, emphasising the design and execution of appropriate research.
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Authors: Daly, Richard T (1974) - -
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Authors: Partee, Barbara Hall (1990) - The section on algebra is presented with an emphasis on lattices as well as Boolean and Heyting algebras. Background for recent research in natural language semantics includes sections on lambda-abstraction and generalized quantifiers. Chapters on automata theory and formal languages contain a discussion of languages between context-free and context-sensitive and form the background for much current work in syntactic theory and computational linguistics. The many exercises not only reinforce basic skills but offer an entry to linguistic applications of mathematical concepts.
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Authors: Yu-Jin Zhang (2023) - This textbook offers advanced content on computer vision (basic content can be found in its prerequisite textbook, "Computer Vision: Principles, Algorithms and Applications", including the basic principles, typical methods and practical techniques. It is intended for graduate courses on related topics, e.g. Computer Vision, 3-D Computer Vision, Graphics, Artificial Intelligence, etc
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Authors: Taweh Beysolow (2018) - After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn't feel that you need be an expert to understand the content
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Authors: Richard Szeliski (2022) - Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of "recipes" this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems.
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Authors: Akshay Kulkarni (2021) - This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms
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Authors: Roozbeh Hazrat (2024) - This textbook introduces Python and its programmingthrough a multitude of clearly presented examples and worked-out exercises.Based on a course taught to undergraduate students of mathematics, science, engineering and finance, the book includes chapters on handling data, calculus, solving equations, and graphics, thus covering all of the basic topics in Python. Each section starts with a description of a new topic and some basic examples. The author then demonstrates the new concepts through worked out exercises. The intention is to enable the reader to learn from the codes, thus avoiding lengthy, exhausting explanations.With its strong focus on programming and problem solving, and an...
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Authors: Zhengtian Wu (2023) - Integer Optimization and its Computation in Emergency Management investigates the computation theory of integer optimization, developing integer programming methods for emergency management and explores related practical applications. Pursuing a holistic approach, this book establishes a fundamental framework for this topic, intended for graduate students who are interested in operations research and optimization, researchers investigating emergency management, and algorithm design engineers working on integer programming or other optimization applications.
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Authors: Mohammed Nurudeen (2024) - This book, Machine Learning with Python: Foundations and Applications, is designed to offer a comprehensive introduction to machine learning using Python. The primary goal is to take readers from the fundamental concepts of machine learning to hands-on practical implementations using real-world examples. Python is the language of choice due to its extensive libraries, simplicity, and relevance in the data science community.
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Authors: Jayaraman Valadi (2024) - The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning. It uncovers the indispensable role of metaheuristics in supervised machine learning algorithms. Ultimately, this book bridges the realms of evolutionary dynamic optimization and machine learning, paving the way for pioneering innovations in the field
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Authors: Daniel Alpay (2024) - This text presents a collection of mathematical exercises with the aim of guiding readers to study topics in statistical physics, equilibrium thermodynamics, information theory, and their various connections. It explores essential tools from linear algebra, elementary functional analysis, and probability theory in detail and demonstrates their applications in topics such as entropy, machine learning, error-correcting codes, and quantum channels. The theory of communication and signal theory are also in the background, and many exercises have been chosen from the theory of wavelets and machine learning. Exercises are selected from a number of different domains, both theoretical and mor...
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Authors: Umberto Michelucci (2024) - This book is for individuals with a scientific background who aspire to apply machine learning within various natural science disciplinessuch as physics, chemistry, biology, medicine, psychology and many more. It elucidates core mathematical concepts in an accessible and straightforward manner, maintaining rigorous mathematical integrity.
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Authors: Charu C. Aggarwal (2024) - The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners
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Authors: Kevin P. Murphy (2022) - "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"
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Authors: Otávio Santana (2024) - The book is divided into four parts, covering essential NoSQL concepts, Java principles, Jakarta EE integration, and the integration of NoSQL databases into enterprise architectures. Readers will explore NoSQL databases, comparing their strengths and use cases. They will then master Java coding principles and design patterns necessary for effective NoSQL integration. The book also discusses the latest Jakarta EE specifications, enhancing readers' understanding of Jakarta's role in data storage and retrieval. Finally, readers will learn to implement various NoSQL databases into enterprise-grade solutions, ensuring security, high availability, and fault tolerance.
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Authors: Jeff Friesen (2024) - Sharpen your Java skills and boost your potential as an IT specialist. This book introduces you to the basic Java features and APIs needed to prepare for a career in programming and development. You'll first receive an introduction to Java and then explore language features ranging from comments though exception/error handling, focusing mainly on language syntax and a few select syntax-related APIs. This constitutes the heart of the book, and you'll use these building blocks to construct simple Java programs, and learn where Java's implementations of expressions (and operators), and statements diverge from other languages.
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Authors: John Tuhao Chen (2024) - "Written by an experienced statistics educator and two data scientists, this book unifies conventional statistical thinking and contemporary machine learning framework into a single overarching umbrella over data science. The book is designed to bridge the knowledge gap between conventional statistics and machine learning. It provides an accessible approach for readers with a basic statistics background to develop a mastery of machine learning. The book starts with elucidating examples in Chapter 1 and fundamentals on refined optimization in Chapter 2, which are followed by common supervised learning methods such as regressions, classification, support vector machines, tree algorithms...
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