Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications

Rajesh Arumugam, Rajalingappaa Shanmugamani

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商品描述

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.

商品描述(中文翻譯)

加強您的自然語言處理應用程式,利用深度學習、NLTK和TensorFlow

主要特點:
- 在各種平台上將神經網絡應用於語言應用程式
- 使用NLTK和TensorFlow執行NLP任務並訓練模型
- 通過強大的深度學習架構(如CNN和RNN)提升您的NLP模型

書籍描述:
自然語言處理(NLP)已在各個領域中找到應用,例如網絡搜索、廣告和客戶服務,並且借助深度學習,我們可以提升其在這些領域中的性能。《Hands-On Natural Language Processing with Python》教您如何利用深度學習模型執行各種NLP任務,以及處理當今NLP挑戰的最佳實踐。

首先,您將了解NLP和深度學習的核心概念,例如卷積神經網絡(CNN)、循環神經網絡(RNN)、語義嵌入、Word2vec等。您將學習如何使用神經網絡執行NLP的每個任務,並在NLP應用程式中訓練和部署神經網絡。您將熟悉在文本分類和序列標記等各種應用領域中使用RNN和CNN,這在情感分析、客戶服務聊天機器人和異常檢測的應用中至關重要。您將獲得實際知識,以使用Python流行的深度學習庫TensorFlow在語言應用程式中實現深度學習。

通過閱讀本書,您將能夠熟練地構建基於深度學習的NLP應用程式,並通過領域專家開發的最佳實踐克服NLP挑戰。

您將學到:
- 實現詞的語義嵌入以進行分類和查找實體
- 通過訓練將詞轉換為向量以執行算術操作
- 訓練深度學習模型以檢測推文和新聞的分類
- 使用搜索和RNN模型實現問答模型
- 使用CNN在各種文本分類數據集上訓練模型
- 實現WaveNet,一種用於生成自然聲音的深度生成模型
- 語音轉文字和文字轉語音
- 使用DeepSpeech訓練模型將語音轉換為文字

本書適合對象:
《Hands-On Natural Language Processing with Python》適合開發人員、機器學習或NLP工程師,他們希望構建利用NLP技術的深度學習應用程式。這本全面的指南對於希望在構建NLP應用程式中擴展深度學習技能的深度學習用戶也很有用。您只需要懂得機器學習和Python的基礎知識即可享受本書。