Natural Language Processing with Python

Boost your NLP models with strong deep learning architectures such as CNNs and RNNs. 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

Natural Language Processing with Python

Steven Bird

O’Reilly Media

2009

Abstract

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.

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.

Contents

  1. Getting Started

  2. Text Classification and POS Tagging Using NLTK

  3. Deep Learning and TensorFlow

  4. Semantic Embedding Using Shallow Models

  5. Text Classification Using LSTM

  6. Searching and DeDuplicating Using CNNs

  7. Named Entity Recognition Using Character LSTM

  8. Text Generation and Summarization Using GRUs

  9. Question-Answering and Chatbots Using Memory Networks

  10. Machine Translation Using the Attention-Based Model

  11. Speech Recognition Using DeepSpeech

  12. Text-to-Speech Using Tacotron

  13. Deploying Trained Models

Citation

Steven Bird, Natural Language Processing with Python,O’Reilly Media, 2009

Collection

Lĩnh vực Công nghệ thông tin

Related document

Natural Language Processing with PythonComputer Organizition And ArchitectureCryptography and Network Security Principles and Practices
Natural Language Processing with PythonComputer Organizition And ArchitectureCryptography and Network Security Principles and Practices

QR code

Natural Language Processing with Python

Content

  • Thứ Tư, 21:09 30/11/2022

Tin tiêu điểm

PGS.TS Nguyễn Thị Hồng Nga, Giám đốc - Trung tâm Đào tạo Sau đại học trao tặng 02 đầu sách ngoại văn cho Trung tâm Thông tin - Thư viện

Thứ Sáu, 07:37 24/05/2024
Hướng dẫn khai thác Bộ sưu tập tài nguyên giáo dục mở (OER)

Hướng dẫn khai thác Bộ sưu tập tài nguyên giáo dục mở (OER)

Thứ Bảy, 15:58 04/05/2024

Truy cập hàng triệu sách điện tử miễn phí với The Online Books Page

Thứ Hai, 08:38 22/01/2024
5 khóa học miễn phí về thiết kế đồ họa

5 khóa học miễn phí về thiết kế đồ họa

Thứ Tư, 09:33 13/12/2023

7 khóa học “Kỹ thuật cơ khí” sinh viên ngành Cơ khí cần biết

Thứ Sáu, 13:57 08/12/2023

Các bài đã đăng

Electric Vehicle Efficient Power and Propulsion Systems

Thứ Ba, 13:54 02/07/2024

Smart Sustainable Manufacturing Systems

Thứ Ba, 13:47 02/07/2024

Green Technology and Renewable Energy Projects

Thứ Ba, 13:40 02/07/2024

Performance and Safety Enhancement Strategies in Vehicle Dynamics and Ground Contac

Thứ Ba, 13:30 02/07/2024

Keeping Autonomous Driving Alive

Thứ Ba, 13:22 02/07/2024

Deep Learning with TensorFlow 2.0 and Keras: Regression, ConvNets, GANs, RNNs, NLP & more with TF 2.0 and the Keras API

Thứ Tư, 20:50 30/11/2022

Giáo trình Kinh tế chính trị

Thứ Tư, 19:42 30/11/2022

Chủ tịch Hồ Chí minh với công cuộc xây dựng và bảo vệ tổ quốc

Thứ Tư, 16:17 30/11/2022

发展汉语:高级阅读 I 第二版 = Developing Chinese: Advanced reading course I

Thứ Tư, 16:08 30/11/2022

发展汉语:高级听力 I (共两册) 第二版 = Developing Chinese: Advanced listening course I. Scripts and answers

Thứ Tư, 16:06 30/11/2022