Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications
暫譯: 實戰自然語言處理與 Python:應用深度學習架構於您的 NLP 應用的實用指南
Rajesh Arumugam, Rajalingappaa Shanmugamani
- 出版商: Packt Publishing
- 出版日期: 2018-07-19
- 售價: $1,830
- 貴賓價: 9.5 折 $1,739
- 語言: 英文
- 頁數: 312
- 裝訂: Paperback
- ISBN: 178913949X
- ISBN-13: 9781789139495
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相關分類:
Python、程式語言、DeepLearning、Text-mining
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相關翻譯:
Python 自然語言處理實戰 (Hands-On Natural Language Processing with Python: A practical guide to applying deep learning architectures to your NLP applications) (簡中版)
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相關主題
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)
**利用深度學習、NLTK 和 TensorFlow 促進您的自然語言處理應用程式**
### 主要特點
- 將神經網絡編織進各種平台的語言應用中
- 使用 NLTK 和 TensorFlow 執行 NLP 任務並訓練其模型
- 使用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)已在各個領域找到應用,例如網頁搜尋、廣告和客戶服務,並且在深度學習的幫助下,我們可以提升其在這些領域的表現。《使用 Python 的實作自然語言處理》教您如何利用深度學習模型執行各種 NLP 任務,以及應對當今 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(Convolutional Neural Networks, CNNs)、遞迴神經網絡(recurrent neural networks, RNNs)、語義嵌入、Word2vec 等。您將學習如何使用神經網絡執行每一個 NLP 任務,並在您的 NLP 應用中訓練和部署神經網絡。您將熟悉在各種應用領域中使用 RNN 和 CNN,例如文本分類和序列標記,這些在情感分析、客戶服務聊天機器人和異常檢測的應用中至關重要。您將獲得實用知識,以便使用 Python 的流行深度學習庫 TensorFlow 在您的語言應用中實現深度學習。
在本書結束時,您將熟練於構建基於深度學習的 NLP 應用,並能夠利用領域專家的最佳實踐克服 NLP 挑戰。
### 您將學到什麼
- 實現詞語的語義嵌入以分類和查找實體
- 通過訓練將詞語轉換為向量以執行算術運算
- 訓練深度學習模型以檢測推文和新聞的分類
- 實現一個結合搜尋和 RNN 模型的問答模型
- 使用 CNN 為各種文本分類數據集訓練模型
- 實現 WaveNet,一種生成自然聲音的深度生成模型
- 將語音轉換為文本,並將文本轉換為語音
- 使用 DeepSpeech 訓練模型將語音轉換為文本
### 本書適合誰
《使用 Python 的實作自然語言處理》適合您,如果您是一位開發者、機器學習或 NLP 工程師,想要構建利用 NLP 技術的深度學習應用。這本全面的指南對於希望擴展其深度學習技能以構建 NLP 應用的深度學習使用者也非常有用。您只需具備機器學習和 Python 的基礎知識,即可享受本書的內容。