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.