Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Introduction to Information Retrieval
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
Foundations of Statistical Natural Language Processing
Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.
Complex Predicates and Information Spreading in LFG
This book provides a simple but precise framework for describing complex predicates and related constructions, and applies it principally to the analysis of complex predicates in Romance, and certain serial verb constructions in Tariana and Miskitu. The authors argue for replacing the projection architecture of LFG with a notion of differential information spreading within a unified feature structure. Another important feature is the use of the conception of argument-structure in Chris Manning's Ergativity to facilitate the description of how complex predicates are assembled. In both of these aspects the result is a framework that preserves the descriptive parsimony of LFG while taking on key ideas from HPSG.
NeuroWisdom
Perfect for readers of How God Changes Your Brain, two researchers present over thirty brain exercises to help readers generate happiness and success, in business and in life. ”This remarkable book translates state-of-the art neuroscience into practical techniques that rapidly promote personal transformation. If you want to double your happiness and your income, start using these powerful brain-changing exercises today!” ―John Assaraf, New York Times bestselling author and CEO of NeuroGym Adapted from a business school course they created for professionals, bestselling author Mark Waldman and Chris Manning present simple brain exercises, based on the latest neuroscience research, to gu...
On the Field with...Peyton and Eli Manning
No other family has conquered football like the Mannings. Discover their amazing story in this biography that includes stats and the achievements of the Mannings, on and off the football field. It all started with the dad, Archie, a former pro quarterback who taught his sons Peyton and Eli to play football. Now, the brothers have a legacy of their own as pro quarterbacks, starting with two stunning Super Bowl wins. This exciting Matt Christopher biography gives readers the story behind this famous football family, as well as thrilling recaps of some of the most awesome games in NFL history.
Natural Language Processing with Transformers, Revised Edition
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how tran...
Human-in-the-Loop Machine Learning
Machine learning applications perform better with human feedback. Keeping the right people in the loop improves the accuracy of models, reduces errors in data, lowers costs, and helps you ship models faster. Human-in-the-loop machine learning lays out methods for humans and machines to work together effectively. You'll find best practices on selecting sample data for human feedback, quality control for human annotations, and designing annotation interfaces. You'll learn to dreate training data for labeling, object detection, and semantic segmentation, sequence labeling, and more. The book starts with the basics and progresses to advanced techniques like transfer learning and self-supervision within annotation workflows.
Dependency Parsing
Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts
Ergativity
None
Machine Learning with TensorFlow, Second Edition
Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural net...
