Transformers for Machine Learning: A Deep Dive (Paperback)
暫譯: 機器學習中的變壓器：深入探討（平裝本）

Kamath, Uday, Graham, Kenneth, Emara, Wael

Transformers for Machine Learning: A Deep Dive (Paperback)
  • 出版商: CRC
  • 出版日期: 2022-05-25
  • 售價: $2,250
  • 貴賓價: 9.5 折 $2,138
  • 語言: 英文
  • 頁數: 257
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 0367767341
  • ISBN-13: 9780367767341
  • 相關分類: Machine Learning

    • 立即出貨 (庫存=1)

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商品描述

Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers.

Key Features:

  • A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers.
  • 60+ transformer architectures covered in a comprehensive manner.
  • A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision.
  • Practical tips and tricks for each architecture and how to use it in the real world.
  • Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab.

The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.

商品描述(中文翻譯)

變壓器（Transformers）正逐漸成為許多神經網絡架構的核心部分，廣泛應用於自然語言處理（NLP）、語音識別、時間序列和計算機視覺等多種應用中。變壓器經歷了許多適應和改變，產生了更新的技術和方法。《機器學習中的變壓器：深入探討》是第一本全面介紹變壓器的書籍。

主要特點：

- 一本全面的參考書，詳細解釋與變壓器相關的每個算法和技術。
- 涵蓋60多種變壓器架構，內容全面。
- 一本幫助理解如何在語音、文本、時間序列和計算機視覺中應用變壓器技術的書籍。
- 每種架構的實用技巧和竅門，以及如何在現實世界中使用它們。
- 實作案例研究和代碼片段，理論與實際的現實分析，使用的工具和庫均可在Google Colab中運行。

最先進的變壓器架構的理論解釋將吸引研究生和研究人員（學術界和業界），因為它提供了一個單一的入口點，深入討論這個快速發展的領域。實作案例研究和代碼將吸引本科生、從業者和專業人士，因為它允許快速實驗並降低進入該領域的門檻。

作者簡介

Uday Kamath has spent more than two decades developing analytics products and combines this experience with learning in statistics, optimization, machine learning, bioinformatics, and evolutionary computing. Uday has contributed to many journals, conferences, and books, is the author of books like XAI: An Introduction to Interpretable XAI, Deep Learning for NLP and Speech Recognition, Mastering Java Machine Learning, and Machine Learning: End-to-End guide for Java developers. He held many senior roles: Chief Analytics Officer for Digital Reasoning, Advisor for
Falkonry, and Chief Data Scientist for BAE Systems Applied Intelligence. Uday has many patents and has built commercial products using AI in domains such as compliance, cybersecurity, financial crime, and bioinformatics. Uday currently works as the Chief Analytics Officer for Smarsh. He is responsible for data science, research of analytical products employing deep learning, transformers, explainable AI, and modern techniques in speech and text for the financial domain and healthcare.

Wael Emara has two decades of experience in academia and industry. Wael has a PhD in Computer Engineering and Computer Science with emphasis on machine learning and artificial intelligence. His technical background and research spans signal and image processing, computer vision, medical imaging, social media analytics, machine learning, and natural language processing. Wael has 10 research publications in various machine learning topics and he is active in the technical community in the greater New York area. Wael currently works as a Senior Research Engineer for Digital Reasoning where he is doing research on state-of-the-art artificial intelligence NLP systems.

Kenneth L. Graham has two decades experience solving quantitative problems in multiple domains, including Monte Carlo simulation, NLP, anomaly detection, cybersecurity, and behavioral profiling. For the past nine years, he has focused on building scalable solutions in NLP for government and industry, including entity coreference resolution, text classification, active learning, and temporal normalization. Kenneth currently works at Smarsh as a Principal Research Engineer, researching how to move state-of the-art NLP methods out of research and into production. Kenneth has five patents for his work in natural language processing, seven research publications, and a Ph.D. in Condensed Matter Physics.

作者簡介(中文翻譯)

Uday Kamath 擁有超過二十年的分析產品開發經驗，並將這些經驗與統計學、優化、機器學習、生物資訊學和演化計算的學習相結合。Uday 曾為許多期刊、會議和書籍做出貢獻，著有《XAI: An Introduction to Interpretable XAI》、《Deep Learning for NLP and Speech Recognition》、《Mastering Java Machine Learning》和《Machine Learning: End-to-End guide for Java developers》等書籍。他曾擔任多個高級職位，包括 Digital Reasoning 的首席分析官、Falkonry 的顧問，以及 BAE Systems Applied Intelligence 的首席數據科學家。Uday 擁有多項專利，並在合規性、網絡安全、金融犯罪和生物資訊學等領域利用人工智慧構建商業產品。Uday 目前擔任 Smarsh 的首席分析官，負責數據科學、深度學習、變壓器、可解釋的人工智慧以及金融領域和醫療保健中語音和文本的現代技術的分析產品研究。

Wael Emara 擁有二十年的學術和產業經驗。Wael 擁有計算機工程和計算機科學的博士學位，專注於機器學習和人工智慧。他的技術背景和研究涵蓋信號和影像處理、計算機視覺、醫學影像、社交媒體分析、機器學習和自然語言處理。Wael 在各種機器學習主題上發表了 10 篇研究論文，並在大紐約地區的技術社群中活躍。Wael 目前擔任 Digital Reasoning 的高級研究工程師，從事最先進的人工智慧自然語言處理系統的研究。

Kenneth L. Graham 擁有二十年的經驗，解決多個領域的定量問題，包括蒙地卡羅模擬、自然語言處理、異常檢測、網絡安全和行為分析。在過去的九年中，他專注於為政府和產業構建可擴展的自然語言處理解決方案，包括實體共指解析、文本分類、主動學習和時間標準化。Kenneth 目前在 Smarsh 擔任首席研究工程師，研究如何將最先進的自然語言處理方法從研究轉移到生產中。Kenneth 擁有五項自然語言處理方面的專利、七篇研究論文，以及凝聚態物理學的博士學位。

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