Deep Learning for Natural Language Processing (Paperback)
暫譯: 深度學習於自然語言處理

Raaijmakers, Stephan

Deep Learning for Natural Language Processing (Paperback)
  • 出版商: Manning
  • 出版日期: 2022-12-06
  • 定價: $2,150
  • 售價: 8.8 折 $1,892 (限時優惠至 2025-03-31)
  • 語言: 英文
  • 頁數: 325
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617295442
  • ISBN-13: 9781617295447
  • 相關分類: DeepLearning

    • 立即出貨 (庫存 < 3)

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商品描述

Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning!

Inside Deep Learning for Natural Language Processing you’ll find a wealth of NLP insights, including:

    An overview of NLP and deep learning
    One-hot text representations
    Word embeddings
    Models for textual similarity
    Sequential NLP
    Semantic role labeling
    Deep memory-based NLP
    Linguistic structure
    Hyperparameters for deep NLP

Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve "human" levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses.

About the book
Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!

What's inside

    Improve question answering with sequential NLP
    Boost performance with linguistic multitask learning
    Accurately interpret linguistic structure
    Master multiple word embedding techniques

About the reader
For readers with intermediate Python skills and a general knowledge of NLP. No experience with deep learning is required.

About the author
Stephan Raaijmakers is professor of Communicative AI at Leiden University and a senior scientist at The Netherlands Organization for Applied Scientific Research (TNO).

商品描述(中文翻譯)

探索自然語言處理中最具挑戰性的問題，並學習如何利用尖端的深度學習來解決這些問題！

在《深度學習與自然語言處理》中，您將發現豐富的NLP見解，包括：

    自然語言處理與深度學習的概述
    一熱編碼文本表示
    詞嵌入
    文本相似性模型
    序列NLP
    語義角色標註
    基於深度記憶的NLP
    語言結構
    深度NLP的超參數

深度學習已將自然語言處理推向令人興奮的新高度和強大的新應用！首次，計算機系統能夠達到“人類”級別的摘要、建立聯繫以及其他需要理解和上下文的任務。《深度學習與自然語言處理》揭示了使這些創新成為可能的突破性技術。Stephan Raaijmakers將他豐富的知識提煉成有用的最佳實踐、實際應用和頂級NLP算法的內部運作。

購買印刷書籍可獲得Manning Publications提供的免費PDF、Kindle和ePub格式電子書。

關於技術
深度學習已改變自然語言處理的領域。神經網絡不僅識別單詞和短語，還識別模式。模型從上下文中推斷意義，並確定情感語調。強大的基於深度學習的NLP模型開啟了潛在用途的金礦。

關於本書
《深度學習與自然語言處理》教您如何使用Python和Keras深度學習庫創建先進的NLP應用。您將學會使用最先進的工具和技術，包括BERT和XLNET、多任務學習以及基於深度記憶的NLP。引人入勝的範例讓您獲得各種實際NLP應用的實作經驗。此外，詳細的代碼討論將向您展示如何將每個範例精確調整為您自己的用途！

內容包括：

    使用序列NLP改善問題回答
    通過語言多任務學習提升性能
    準確解釋語言結構
    精通多種詞嵌入技術

關於讀者
適合具備中級Python技能和一般NLP知識的讀者。不需要具備深度學習經驗。

關於作者
Stephan Raaijmakers是萊頓大學的傳播人工智慧教授，也是荷蘭應用科學研究組織（TNO）的高級科學家。

作者簡介

Stephan Raaijmakers is a senior scientist at TNO and holds a PhD in machine learning and text analytics. He's the technical coordinator of two large European Union-funded research security-related projects. He's currently anticipating an endowed professorship in deep learning and NLP at a major Dutch university.

作者簡介(中文翻譯)

Stephan Raaijmakers 是 TNO 的資深科學家，擁有機器學習和文本分析的博士學位。他是兩個大型歐盟資助的研究安全相關項目的技術協調員。目前，他正期待在荷蘭一所主要大學獲得深度學習和自然語言處理的特聘教授職位。

目錄大綱

PART 1 INTRODUCTION
1 Deep learning for NLP
2 Deep learning and language: The basics
3 Text embeddings
PART 2 DEEP NLP
4 Textual similarity
5 Sequential NLP
6 Episodic memory for NLP
PART 3 ADVANCED TOPICS
7 Attention
8 Multitask learning
9 Transformers
10 Applications of Transformers: Hands-on with BERT

目錄大綱(中文翻譯)

PART 1 INTRODUCTION

1 Deep learning for NLP

2 Deep learning and language: The basics

3 Text embeddings

PART 2 DEEP NLP

4 Textual similarity

5 Sequential NLP

6 Episodic memory for NLP

PART 3 ADVANCED TOPICS

7 Attention

8 Multitask learning

9 Transformers

10 Applications of Transformers: Hands-on with BERT

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