Name: Neural Representations of Natural Language (Signals and Communication Technology) (en Inglés)
Price: 173760.00 CLP
Availability: InStock
Author: Lyndon White; Roberto Togneri; Wei Liu
ISBN: 9789811300615

portada Neural Representations of Natural Language (Signals and Communication Technology) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Lyndon White; Roberto Togneri; Wei Liu

Año

2018

Idioma

Inglés

N° páginas

122

Encuadernación

Tapa Dura

ISBN13

9789811300615

N° edición

Categorías

Reconocimiento de formas
Visión por ordenador

Neural Representations of Natural Language (Signals and Communication Technology) (en Inglés)

Lyndon White; Roberto Togneri; Wei Liu (Autor) · Springer · Tapa Dura

Libro Nuevo

$ 173.760

$ 241.340

Ahorras: $ 67.580

28% descuento

Envío normal

Origen: Estados Unidos (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Viernes 17 de Mayo y el Martes 28 de Mayo.

Lo recibirás en cualquier lugar de Chile entre 1 y 3 días hábiles luego del envío.

Reseña del libro "Neural Representations of Natural Language (Signals and Communication Technology) (en Inglés)"

This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other.