Natural Language Processing with TensorFlow: The definitive NLP book to implement the most sought-after machine learning models and tasks, 2/e (Paperback)
暫譯: 使用 TensorFlow 的自然語言處理:實現最受歡迎的機器學習模型和任務的權威 NLP 書籍,第二版(平裝本)

Ganegedara, Thushan

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商品描述

From introductory NLP tasks to Transformer models, this new edition teaches you to utilize powerful TensorFlow APIs to implement end-to-end NLP solutions driven by performant ML (Machine Learning) models

 

Key Features:

  • Learn to solve common NLP problems effectively with TensorFlow 2.x
  • Implement end-to-end data pipelines guided by the underlying ML model architecture
  • Use advanced LSTM techniques for complex data transformations, custom models and metrics

 

Book Description:

Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures.

 

The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.

 

TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.

 

By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you'll be able to confidently use TensorFlow throughout your machine learning workflow.

 

What You Will Learn:

  • Learn core concepts of NLP and techniques with TensorFlow
  • Use statee-of-the-art Transformers and how they are used to solve NLP tasks
  • Perform sentence classification and text generation using CNNs and RNNS
  • Utilize advanced models for machine translation and image caption generation
  • Build end-to-end data pipelines in TensorFlow
  • Learn interesting facts and practices related to the task at hand
  • Create word representations of large amounts of data for deep learning

 

Who this book is for:

This book is for Python developers and programmers with a strong interest in deep learning, who want to learn how to leverage TensorFlow to simplify NLP tasks.

 

Fundamental Python skills are assumed, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. No previous natural language processing experience required.

商品描述(中文翻譯)

從入門的自然語言處理 (NLP) 任務到 Transformer 模型,本新版本教您如何利用強大的 TensorFlow API 實現由高效能機器學習 (ML) 模型驅動的端到端 NLP 解決方案

主要特點:


  • 學習如何有效地使用 TensorFlow 2.x 解決常見的 NLP 問題

  • 根據底層的 ML 模型架構實現端到端數據管道

  • 使用先進的 LSTM 技術進行複雜的數據轉換、自定義模型和指標

書籍描述:

學習如何解決自然語言處理 (NLP) 問題是一項重要的技能,因為數據的爆炸性增長與對生產環境中機器學習解決方案的需求相結合。《使用 TensorFlow 的自然語言處理 (第二版)》將教您如何使用各種深度學習模型架構解決常見的現實世界 NLP 問題。

本書首先讓讀者熟悉 NLP 和 TensorFlow 的基本知識。然後,逐步教您 TensorFlow 2.x 的不同面向。在接下來的章節中,您將學習如何生成強大的詞向量、分類文本、生成新文本以及生成圖像標題等現實世界 NLP 的其他令人興奮的應用案例。

TensorFlow 已經發展成為一個支持機器學習工作流程的生態系統,通過攝取和轉換數據、構建模型、監控和生產化。我們將直接從文件中讀取文本,並通過 TensorFlow 數據管道執行所需的轉換。我們還將看到如何使用一個多功能的可視化工具 TensorBoard 來可視化我們的模型。

到本書結束時,您將能夠熟練使用 TensorFlow 構建具有多種不同架構的深度學習模型,並有效地使用 TensorFlow 攝取數據。此外,您將能夠自信地在整個機器學習工作流程中使用 TensorFlow。

您將學到的內容:


  • 學習 NLP 的核心概念和使用 TensorFlow 的技術

  • 使用最先進的 Transformers 及其在解決 NLP 任務中的應用

  • 使用 CNN 和 RNN 進行句子分類和文本生成

  • 利用先進模型進行機器翻譯和圖像標題生成

  • 在 TensorFlow 中構建端到端數據管道

  • 學習與當前任務相關的有趣事實和實踐

  • 為深度學習創建大量數據的詞表示

本書適合誰:

本書適合對深度學習有濃厚興趣的 Python 開發者和程序員,想要學習如何利用 TensorFlow 簡化 NLP 任務。

假設具備基本的 Python 技能,以及機器學習和本科級微積分及線性代數的基本知識。不需要先前的自然語言處理經驗。