Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand
暫譯: 企業中的應用自然語言處理：教導機器閱讀、寫作與理解

Patel, Ankur A., Arasanipalai, Ajay Uppili

Applied Natural Language Processing in the Enterprise: Teaching Machines to Read, Write, and Understand
  • 出版商: O'Reilly
  • 出版日期: 2021-06-15
  • 定價: $2,640
  • 售價: 8.8 折 $2,323 (限時優惠至 2025-03-31)
  • 語言: 英文
  • 頁數: 336
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 149206257X
  • ISBN-13: 9781492062578
  • 相關分類: Python、程式語言、人工智慧、Text-mining

    • 立即出貨 (庫存 < 3)

買這商品的人也買了...

商品描述

NLP has exploded in popularity over the last few years. But while Google, Facebook, OpenAI, and others continue to release larger language models, many teams still struggle with building NLP applications that live up to the hype. This hands-on guide helps you get up to speed on the latest and most promising trends in NLP.

With a basic understanding of machine learning and some Python experience, you'll learn how to train and deploy real-world NLP applications in your organization. Authors Ankur Patel and Ajay Uppili Arasanipalai guide you through the process using code and examples that highlight the best practices in modern NLP.

  • Use state-of-the-art NLP models such as BERT and GPT-3 to solve NLP tasks such as named entity recognition, text classification, semantic search, and reading comprehension
  • Train NLP models with performance comparable or superior to that of out-of-the-box systems
  • Learn about transformer architecture and modern tricks like transfer learning that have taken the NLP world by storm
  • Become familiar with the tools of the trade, including spaCy, Hugging Face, and fast.ai
  • Use Python and PyTorch to build core parts of the NLP pipeline from scratch, including tokenizers, embeddings, and language models
  • Take your models out of Jupyter notebooks and learn how to deploy, monitor, and maintain them in production

商品描述(中文翻譯)

NLP 在過去幾年中迅速流行起來。然而，儘管 Google、Facebook、OpenAI 等公司持續推出更大型的語言模型，許多團隊仍然在構建能夠實現這些期望的 NLP 應用程序方面面臨挑戰。本手冊將幫助您了解 NLP 中最新和最具前景的趨勢。

擁有基本的機器學習知識和一些 Python 經驗後，您將學會如何在您的組織中訓練和部署實際的 NLP 應用程序。作者 Ankur Patel 和 Ajay Uppili Arasanipalai 將通過代碼和示例引導您了解這一過程，並突顯現代 NLP 的最佳實踐。

- 使用最先進的 NLP 模型，如 BERT 和 GPT-3，解決命名實體識別、文本分類、語義搜索和閱讀理解等 NLP 任務
- 訓練性能可與現成系統相媲美或更優的 NLP 模型
- 了解變壓器架構和現代技巧，如轉移學習，這些技術在 NLP 領域引起了轟動
- 熟悉行業工具，包括 spaCy、Hugging Face 和 fast.ai
- 使用 Python 和 PyTorch 從零開始構建 NLP 流程的核心部分，包括分詞器、嵌入和語言模型
- 將您的模型從 Jupyter notebooks 中取出，學習如何在生產環境中部署、監控和維護它們

作者簡介

Ankur A. Patel is the Co-Founder and Head of Data at Glean and the Co-Founder of Mellow. Glean uses NLP to extract data from invoices and generate vendor spend intelligence for clients. Mellow is on a mission to democratize NLP tasks such as entity resolution, named entity recognition, and text classification for everyone. Previously, Ankur led teams at 7Park Data, ThetaRay, and R-Squared Macro and began his career at Bridgewater Associates and J.P. Morgan. He is a graduate of Princeton University and lives in New York City.

Ajay Arasanipalai is a deep learning researcher and student at University of Illinois at Urbana-Champaign. He's authored many popular articles that discuss state-of-the-art deep learning research. In March 2018, Ajay was invited to speak about accelerated deep learning at Think 2018, IBM's largest annual tech conference. Currently, as cochair of the ACM SIGAI chapter at the University of Illinois, he organizes educational workshops and projects for undergraduate students.

作者簡介(中文翻譯)

Ankur A. Patel 是 Glean 的共同創辦人及數據負責人，也是 Mellow 的共同創辦人。Glean 利用自然語言處理 (NLP) 從發票中提取數據，並為客戶生成供應商支出智能。Mellow 的使命是讓每個人都能平等地使用 NLP 任務，例如實體解析、命名實體識別和文本分類。之前，Ankur 在 7Park Data、ThetaRay 和 R-Squared Macro 領導團隊，並在 Bridgewater Associates 和 J.P. Morgan 開始他的職業生涯。他是普林斯頓大學的畢業生，現居紐約市。

Ajay Arasanipalai 是伊利諾伊大學香檳分校的深度學習研究員和學生。他撰寫了許多討論最先進深度學習研究的熱門文章。在 2018 年 3 月，Ajay 受邀在 Think 2018，IBM 最大的年度技術會議上發表有關加速深度學習的演講。目前，作為伊利諾伊大學 ACM SIGAI 分會的共同主席，他為本科生組織教育工作坊和項目。

類似商品