A comprehensive exploration of Andrew Viterbi's groundbreaking 1967 algorithm that revolutionized sequence decoding. Learn how dynamic programming made optimal inference in Hidden Markov Models computationally feasible, transforming speech recognition, part-of-speech tagging, and sequence labeling tasks in natural language processing.
This article is part of the free-to-read History of Language AI book
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About the author: Michael Brenndoerfer
All opinions expressed here are my own and do not reflect the views of my employer.
Michael currently works as an Associate Director of Data Science at EQT Partners in Singapore, where he drives AI and data initiatives across private capital investments.
With over a decade of experience spanning private equity, management consulting, and software engineering, he specializes in building and scaling analytics capabilities from the ground up. He has published research in leading AI conferences and holds expertise in machine learning, natural language processing, and value creation through data.
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