Hands-On Natural Language Processing with Python : (Record no. 30804)

MARC details
000 -LEADER
fixed length control field 03454nam a2200241Ia 4500
001 - CONTROL NUMBER
control field 41578
003 - CONTROL NUMBER IDENTIFIER
control field IN-BdCUP
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230421155130.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230413s2023 000 0 eng
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781789139495
040 ## - CATALOGING SOURCE
Language of cataloging eng
Transcribing agency IN-BdCUP
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.133
Item number ARU
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Arumugam, Rajesh
245 #0 - TITLE STATEMENT
Title Hands-On Natural Language Processing with Python :
Remainder of title A practical guide to applying deep learning architectures to your nlp applications /
Statement of responsibility, etc. Arumugam, Rajesh & Shanmugmani, Rajalingappaa
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Burmingham :
Name of publisher, distributor, etc. Packet Publishing,
Date of publication, distribution, etc. 2018.
300 ## - PHYSICAL DESCRIPTION
Extent vi, 312 p. ;
Dimensions 24 cm.
520 ## - SUMMARY, ETC.
Summary, etc. Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today's NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computers
Topical term or geographic name entry element Natural Language--Python
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Shanmugmani, Rajalingappaa
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Bill number Total checkouts Full call number Barcode Date last seen Actual Cost, replacement price Bill Date Koha item type
    Dewey Decimal Classification     Ranganathan Library Ranganathan Library 17/03/2020 K. K. Distributors, Delhi 1099.00 4113   005.133 ARU 038227 13/04/2023 770.00 02/02/2020 Book
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