Hands-On Question Answering Systems with Bert: Applications in Neural Networks and Natural Language Processing
暫譯: 實作問答系統與Bert：神經網絡與自然語言處理的應用

Sabharwal, Navin, Agrawal, Amit

Hands-On Question Answering Systems with Bert: Applications in Neural Networks and Natural Language Processing
  • 出版商: Apress
  • 出版日期: 2021-01-13
  • 售價: $1,575
  • 貴賓價: 9.5 折 $1,496
  • 語言: 英文
  • 頁數: 184
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484266633
  • ISBN-13: 9781484266632
  • 相關分類: 人工智慧、DeepLearning、Text-mining

    • 立即出貨 (庫存=1)

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商品描述

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.

The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT.

After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system.

Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT.

What You Will Learn

  • Examine the fundamentals of word embeddings
  • Apply neural networks and BERT for various NLP tasks
  • Develop a question-answering system from scratch
  • Train question-answering systems for your own data

Who This Book Is For

AI and machine learning developers and natural language processing developers.

 

商品描述(中文翻譯)

獲得有關如何使用 BERT（雙向編碼器表示法來自變壓器）來開發問答（QA）系統的實踐知識，這是通過自然語言處理（NLP）和深度學習來實現的。

本書首先概述了 BERT 背後的技術背景。接著介紹 NLP 的基本概念，包括通過標記化、詞幹提取和詞形還原進行的自然語言理解，以及詞袋模型。然後，您將了解用於 NLP 的神經網絡，從其變體開始，例如遞歸神經網絡、編碼器和解碼器、雙向編碼器和解碼器，以及變壓器模型。在此過程中，您將涵蓋詞嵌入及其類型，以及 BERT 的基本概念。

在這個堅實的基礎之後，您將準備深入研究 BERT 算法，例如掩碼語言模型和下一句預測。您將看到不同的 BERT 變體，隨後是一個問答系統的實作範例。

《使用 BERT 的實作問答系統》是希望使用 BERT 開發和設計 NLP 系統的開發者和數據科學家的良好起點。它提供了使用 BERT 的逐步指導。

您將學到的內容：

- 檢視詞嵌入的基本原理
- 將神經網絡和 BERT 應用於各種 NLP 任務
- 從零開始開發問答系統
- 為您自己的數據訓練問答系統

本書適合對象：

人工智慧和機器學習開發者以及自然語言處理開發者。

作者簡介

Navin is the chief architect for HCL DryICE Autonomics. He is an innovator, thought leader, author, and consultant in the areas of AI, machine learning, cloud computing, big data analytics, and software product development. He is responsible for IP development and service delivery in the areas of AI and machine learning, automation, AIOPS, public cloud GCP, AWS, and Microsoft Azure. Navin has authored 15+ books in the areas of cloud computing, cognitive virtual agents, IBM Watson, GCP, containers, and microservices.

Amit Agrawal is a senior data scientist and researcher delivering solutions in the fields of AI and machine learning. He is responsible for designing end-to-end solutions and architecture for enterprise products. He has also authored and reviewed books in the area of cognitive virtual assistants.

 

 

作者簡介(中文翻譯)

Navin 是 HCL DryICE Autonomics 的首席架構師。他是人工智慧（AI）、機器學習、雲端運算、大數據分析和軟體產品開發領域的創新者、思想領袖、作者和顧問。他負責 AI 和機器學習、自動化、AIOPS、公共雲 GCP、AWS 和 Microsoft Azure 的知識產權開發和服務交付。Navin 已經在雲端運算、認知虛擬代理、IBM Watson、GCP、容器和微服務等領域撰寫了 15 本以上的書籍。

Amit Agrawal 是一位資深數據科學家和研究員，專注於提供人工智慧和機器學習領域的解決方案。他負責設計企業產品的端到端解決方案和架構。他也在認知虛擬助手領域撰寫和審核過書籍。

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