Transformers for Machine Learning av Uday Kamath, Kenneth Graham, Wael Emara

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<P>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. <B><I>Transformers for Machine Learning: A Deep Dive</I></B> is the first comprehensive book on transformers.</P><P>Key Features:</P><UL><LI>A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers.</LI><LI>60+ transformer architectures covered in a comprehensive manner.</LI><LI>A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision.</LI><LI>Practical tips and tricks for each architecture and how to use it in the real world.</LI><LI>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 Co