Transformers for Machine Learning: A Deep Dive (Paperback)

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.

「Transformer」已成為許多神經網絡架構的核心組件，被廣泛應用於自然語言處理、語音識別、時間序列和計算機視覺等各種應用領域。Transformer經歷了許多改進和變化，產生了新的技術和方法。《Transformer for Machine Learning: A Deep Dive》是第一本全面介紹Transformer的書籍。

主要特點：
- 詳細解釋與Transformer相關的每個算法和技術的綜合參考書籍。
- 詳細介紹60多種Transformer架構。
- 介紹如何在語音、文本、時間序列和計算機視覺中應用Transformer技術的書籍。
- 提供每個架構的實用技巧和技巧，以及如何在實際應用中使用它。
- 提供理論和實際應用的案例研究和代碼片段，使用工具和庫進行實際的分析，並可在Google Colab上運行。

對於研究生學生和研究人員（學術界和工業界），本書的理論解釋將提供一個深入討論這個快速發展領域的單一入口。對於本科生、從業人員和專業人士來說，實際的案例研究和代碼將有吸引力，因為它可以進行快速實驗，降低進入這個領域的門檻。

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.

Uday Kamath已經花費超過二十年的時間開發分析產品，並結合統計學、優化、機器學習、生物信息學和演化計算等領域的學習經驗。Uday在許多期刊、會議和書籍中有貢獻，是《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》等書籍的作者。他曾擔任Digital Reasoning的首席分析官、Falkonry的顧問以及BAE Systems Applied Intelligence的首席數據科學家等高級職位。Uday擁有許多專利，並使用人工智能在合規性、網絡安全、金融犯罪和生物信息學等領域建立商業產品。目前，Uday在Smarsh擔任首席分析官，負責數據科學、深度學習、轉換器、可解釋人工智能以及金融領域和醫療保健領域的現代技術的分析產品研究。

Wael Emara在學術界和工業界擁有二十年的經驗。他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能。他的技術背景和研究涵蓋了信號和圖像處理、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等領域。Wael在各種機器學習主題上有10篇研究論文，並在紐約地區的技術社區活躍。目前，Wael在Digital Reasoning擔任高級研究工程師，研究最先進的人工智能自然語言處理系統。

Kenneth L. Graham在多個領域解決量化問題已有二十年的經驗，包括蒙特卡羅模擬、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，他專注於為政府和工業界建立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、主動學習和時間規範化。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應用於實際生產中。他在自然語言處理方面擁有五項專利、七篇研究論文和凝聚態物理學的博士學位。