Advanced Concepts in Transformers for Deep Learning

Prasanth Yadla is a Senior Machine Learning Engineer specializing in deep learning research, based in Seattle, Washington, USA. His research interests include natural language processing, multimodal deep learning, speech processing, and large-scale generative AI models.

He has more than six years of industry experience across leading technology companies including Amazon Alexa, Cloudera, and Oracle Corporation. His work has spanned open-domain question answering models for Alexa AI, recommendation systems for Amazon Music, and developing large-scale machine learning platforms and infrastructure.

His research contributions have been published at premier deep learning venues and journals, including ACL, CVPR, ICCV, ICLR, IJCAI and other leading peer-reviewed conferences and publications. He has also served as a reviewer and program committee member for prominent AI conferences and contributed as a session chair at international research events.

Prasanth holds a Master of Science in Computer Science with a concentration in Deep Learning from North Carolina State University, Raleigh, NC, USA, along with a Master of Science (Hons.) in Physics and a Bachelor of Engineering (Hons.) in Computer Science from BITS Pilani, India. He is a Senior Member of IEEE.

Introduction to Transformers.- Anatomy of a Transformer.- Advanced Attention Mechanisms.- Transformer Variants and Extensions.- Transformers in Natural Language Processing (NLP).- Transformers Beyond Text.- Training Transformers at Scale.- Advanced Optimization Techniques.- Interpreting and Explaining Transformers.- Transformers in Research and Development.- State Space Models and Hybrid Architectures.- Advanced Prompting Techniques.- Retrieval-Augmented Generation.- Multi-Agent Systems and Tool Use.- Speculative Decoding and Inference Optimization.- Hands-On with Transformers.- Conclusion.