1. DSpace at My University

Search

Filters:

BROWSE

Has File(s)

Search Results

Results 171-180 of 214 (Search time: 0.1 seconds).
Item hits:
  • Sách/Book

  • Mathematical methods in linguistics

    • 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.
  • Sách/Book

  • Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing

    • 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
  • Sách/Book

  • An introduction to second language acquisition research

    • 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.
  • Sách/Book

  • Natural language processing recipes : unlocking text data with machine learning and deep learning using Python

    • 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
  • Sách/Book

  • Language development and neurological theory

    • Authors: - (1977)

  • This book is addressed to researchers and students of the neuropsychology of language, whether they call themselves psychologists, neuropsychologists, neurologists, or linguists.
  • Sách/Book

  • 3-D computer vision : principles, algorithms and applications

    • 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
  • Sách/Book

  • Computer vision : algorithms and applications

    • 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.
  • Sách/Book

  • Probability

    • Authors: A.N. Shiryaev (1996)

  • This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, ergodic theory, weak convergence of probability measures, stationary stochastic processes, and the Kalman-Bucy filter. Many examples are discussed in detail, and there are a large number of exercises. The book is accessible to advanced undergraduates and can be used as a text for self-study