Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applicati
暫譯: 實作 Python 自然語言處理：探索分析和處理文本的工具與技術，以建立實際的 NLP 應用程式

Aman Kedia , Mayank Rasu

Hands-On Python Natural Language Processing: Explore tools and techniques to analyze and process text with a view to building real-world NLP applicati
  • 出版商: Packt Publishing
  • 出版日期: 2020-06-26
  • 售價: $1,360
  • 貴賓價: 9.5 折 $1,292
  • 語言: 英文
  • 頁數: 316
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838989595
  • ISBN-13: 9781838989590
  • 相關分類: Python、程式語言、Text-mining

    • 立即出貨 (庫存=1)

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商品描述

Key Features

  • Perform various NLP tasks to build linguistic applications using Python libraries
  • Understand, analyze, and generate text to provide accurate results
  • Interpret human language using various NLP concepts, methodologies, and tools

Book Description

Natural Language Processing (NLP) is the subfield in computational linguistics that enables computers to understand, process, and analyze text. This book caters to the unmet demand for hands-on training of NLP concepts and provides exposure to real-world applications along with a solid theoretical grounding.

This book starts by introducing you to the field of NLP and its applications, along with the modern Python libraries that you'll use to build your NLP-powered apps. With the help of practical examples, you'll learn how to build reasonably sophisticated NLP applications, and cover various methodologies and challenges in deploying NLP applications in the real world. You'll cover key NLP tasks such as text classification, semantic embedding, sentiment analysis, machine translation, and developing a chatbot using machine learning and deep learning techniques. The book will also help you discover how machine learning techniques play a vital role in making your linguistic apps smart. Every chapter is accompanied by examples of real-world applications to help you build impressive NLP applications of your own.

By the end of this NLP book, you'll be able to work with language data, use machine learning to identify patterns in text, and get acquainted with the advancements in NLP.

What you will learn

  • Understand how NLP powers modern applications
  • Explore key NLP techniques to build your natural language vocabulary
  • Transform text data into mathematical data structures and learn how to improve text mining models
  • Discover how various neural network architectures work with natural language data
  • Get the hang of building sophisticated text processing models using machine learning and deep learning
  • Check out state-of-the-art architectures that have revolutionized research in the NLP domain

Who this book is for

This NLP Python book is for anyone looking to learn NLP's theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.

商品描述(中文翻譯)

**主要特點**

- 使用 Python 函式庫執行各種自然語言處理 (NLP) 任務，以建立語言應用程式
- 理解、分析和生成文本，以提供準確的結果
- 使用各種 NLP 概念、方法論和工具來解釋人類語言

**書籍描述**

自然語言處理 (NLP) 是計算語言學的一個子領域，使計算機能夠理解、處理和分析文本。本書滿足了對 NLP 概念實作訓練的需求，並提供了現實世界應用的接觸以及堅實的理論基礎。

本書首先介紹 NLP 領域及其應用，並介紹您將用來建立 NLP 驅動應用程式的現代 Python 函式庫。在實際範例的幫助下，您將學習如何建立相對複雜的 NLP 應用程式，並涵蓋在現實世界中部署 NLP 應用程式的各種方法論和挑戰。您將涵蓋關鍵的 NLP 任務，如文本分類、語義嵌入、情感分析、機器翻譯，以及使用機器學習和深度學習技術開發聊天機器人。本書還將幫助您發現機器學習技術在使您的語言應用程式智能化方面所扮演的重要角色。每一章都附有現實世界應用的範例，幫助您建立自己的令人印象深刻的 NLP 應用程式。

在本書結束時，您將能夠處理語言數據，使用機器學習識別文本中的模式，並熟悉 NLP 的最新進展。

**您將學到的內容**

- 理解 NLP 如何驅動現代應用程式
- 探索關鍵的 NLP 技術以建立您的自然語言詞彙
- 將文本數據轉換為數學數據結構，並學習如何改善文本挖掘模型
- 發現各種神經網絡架構如何處理自然語言數據
- 熟悉使用機器學習和深度學習建立複雜的文本處理模型
- 了解在 NLP 領域中徹底改變研究的最先進架構

**本書適合誰**

這本 NLP Python 書籍適合任何希望學習 NLP 理論和實踐方面的人。它從基礎開始，逐步涵蓋高級概念，使不同 NLP 熟練程度的讀者都能輕鬆跟隨。這本全面的指南將幫助您深入理解建立語言應用程式的 NLP 方法論；不過，預期讀者需具備 Python 程式語言的工作知識和高中數學水平。

作者簡介

Aman Kedia is a data enthusiast and lifelong learner. He is an avid believer in Artificial Intelligence (AI) and the algorithms supporting it. He has worked on state-of-the-art problems in Natural Language Processing (NLP), encompassing resume matching and digital assistants, among others. He has worked at Oracle and SAP, trying to solve problems leveraging advancements in AI. He has four published research papers in the domain of AI.

Mayank Rasu has more than 12 years of global experience as a data scientist and quantitative analyst in the investment banking industry. He has worked at the intersection of finance and technology and has developed and deployed AI-based applications within the finance domain. His experience includes building sentiment analyzers, robotics, and deep learning-based document review, among many others areas.

作者簡介(中文翻譯)

Aman Kedia 是一位數據愛好者和終身學習者。他堅信人工智慧 (Artificial Intelligence, AI) 及其背後的演算法。他曾在自然語言處理 (Natural Language Processing, NLP) 的尖端問題上工作，包括履歷匹配和數位助理等。他曾在 Oracle 和 SAP 工作，致力於利用 AI 的進步來解決問題。他在 AI 領域發表了四篇研究論文。

Mayank Rasu 在投資銀行業擁有超過 12 年的全球經驗，擔任數據科學家和量化分析師。他在金融與科技的交匯處工作，並在金融領域開發和部署基於 AI 的應用程式。他的經驗包括構建情感分析器、機器人技術和基於深度學習的文件審查等多個領域。

目錄大綱

  1. Understanding the Basics of NLP
  2. NLP Using Python
  3. Building your NLP Vocabulary
  4. Transforming Text into Data Structures
  5. Word Embeddings and Distance Measurements for Text
  6. Exploring Sentence-, Document-, and Character-Level Embeddings
  7. Identifying Patterns in Text using Machine Learning
  8. From Human Neurons to Artificial Neurons for Understanding Text
  9. Applying Convolutions to Text
  10. Capturing Temportal Relationships in Text
  11. State of the Art in NLP

目錄大綱(中文翻譯)


  1. Understanding the Basics of NLP

  2. NLP Using Python

  3. Building your NLP Vocabulary

  4. Transforming Text into Data Structures

  5. Word Embeddings and Distance Measurements for Text

  6. Exploring Sentence-, Document-, and Character-Level Embeddings

  7. Identifying Patterns in Text using Machine Learning

  8. From Human Neurons to Artificial Neurons for Understanding Text

  9. Applying Convolutions to Text

  10. Capturing Temportal Relationships in Text

  11. State of the Art in NLP

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