Advanced Natural Language Processing with TensorFlow 2: Build real-world effective NLP applications using NER, RNNs, seq2seq models, Transformers, and

Bansal, Ashish

  • 出版商: Packt Publishing
  • 出版日期: 2021-02-03
  • 售價: $1,440
  • 貴賓價: 9.5$1,368
  • 語言: 英文
  • 頁數: 380
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800200935
  • ISBN-13: 9781800200937
  • 相關分類: DeepLearningTensorFlowText-mining
  • 立即出貨 (庫存=1)



One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks

Key Features

  • Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2
  • Explore applications like text generation, summarization, weakly supervised labelling and more
  • Read cutting edge material with seminal papers provided in the GitHub repository with full working code

Book Description

Recently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques.

The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs.

The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2.

Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece.

By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems.

What you will learn

  • Grasp important pre-steps in building NLP applications like POS tagging
  • Use transfer and weakly supervised learning using libraries like Snorkel
  • Do sentiment analysis using BERT
  • Apply encoder-decoder NN architectures and beam search for summarizing texts
  • Use Transformer models with attention to bring images and text together
  • Build apps that generate captions and answer questions about images using custom Transformers
  • Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models

Who this book is for

This is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra.

The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.




- 使用TensorFlow 2應用深度學習算法和技術,如BiLSTMS、CRFs、BPE等。
- 探索應用領域,如文本生成、摘要、弱監督標記等。
- 在GitHub存儲庫中提供具有完整工作代碼的重要論文。



本書專注於NLP、語言生成和對話系統等領域的創新應用。它幫助您應用文本預處理的概念,使用Stanford NLP和SpaCy等流行庫進行分詞、詞性標註和詞形還原。您將使用條件隨機場和Viterbi解碼在RNN之上從頭開始構建命名實體識別(NER)。

本書涵蓋了生成用於句子完成和文本摘要的文本的關鍵新興領域,通過為圖像生成標題來橋接圖像和文本,以及管理聊天機器人的對話方面。您將學習如何使用TensorFlow 2進行轉移學習和微調。




- 掌握構建NLP應用的重要前置步驟,如詞性標註。
- 使用Snorkel等庫進行轉移學習和弱監督學習。
- 使用BERT進行情感分析。
- 應用編碼器-解碼器神經網絡架構和束搜索來進行文本摘要。
- 使用注意力的Transformer模型將圖像和文本結合在一起。
- 使用自定義Transformer構建生成圖像標題和回答圖像問題的應用程序。
- 使用高級TensorFlow技術,如學習率退火、自定義層和自定義損失函數,構建最新的DeepNLP模型。





Ashish is an AI/ML leader, a well-known speaker, and an astute technologist with over 20 years of experience in the field. He has a Bachelor's in technology from IIT BHU, and an MBA in marketing from Kellogg School of Management. He is currently the Director of Recommendations at Twitch where he works on building scalable recommendation systems across a variety of product surfaces, connecting content to people. He has worked on recommendation systems at multiple organizations, most notably Twitter where he led Trends and Events recommendations and at Capital One where he worked on B2B and B2C products. Ashish was also a co-founder of GALE Partners, a full-service digital agency in Toronto, and spent over 9 years at SapientNitro, a leading digital agency.


Ashish 是一位人工智慧/機器學習領域的領導者,著名演講者和精明的技術專家,擁有超過20年的經驗。他在印度理工學院班加羅爾分校獲得技術學士學位,並在凱洛格管理學院獲得市場營銷碩士學位。他目前擔任 Twitch 的推薦系統總監,負責在各種產品平台上建立可擴展的推薦系統,將內容與用戶相連接。他曾在多個組織工作過推薦系統,尤其是在 Twitter,他領導了趨勢和事件推薦,以及在 Capital One,他參與了B2B和B2C產品的開發。Ashish 也是 GALE Partners 的聯合創始人,這是一家位於多倫多的全方位數位代理商,並在 SapientNitro,一家領先的數位代理商工作了超過9年。


  1. Essentials of NLP
  2. Understanding Sentiment in Natural Language with BiLSTMs
  3. Named Entity Recognition (NER) with BiLSTMs, CRFs and Viterbi Decoding
  4. Transfer Learning with BERT
  5. Generating Text with RNNs and GPT-2
  6. Text Summarization with Seq2seq Attention and Transformer Networks
  7. Multi-Modal Networks and Image Captioning with ResNets and Transformer Networks
  8. Weakly Supervised Learning for Classification with Snorkel
  9. Building Conversational AI Applications with Deep Learning
  10. Installation and Setup Instructions for Code


- Essentials of NLP
- 使用 BiLSTMs 了解自然語言中的情感
- 使用 BiLSTMs、CRFs 和 Viterbi 解碼進行命名實體識別(NER)
- 使用 BERT 進行轉移學習
- 使用 RNNs 和 GPT-2 生成文本
- 使用 Seq2seq Attention 和 Transformer 網絡進行文本摘要
- 使用 ResNets 和 Transformer 網絡進行多模態網絡和圖像標題生成
- 使用 Snorkel 進行弱監督學習進行分類
- 使用深度學習構建對話式 AI 應用
- 代碼的安裝和設置指南