Deep learning does not always mean adopting a radically new approach and throwing away existing solutions. Very often, existing solutions have been developed over time with significant budget and efforts and have a significant amount of accumulated assets, such as software, institutional knowledge, business processes, talent pools, and community. Thus, organizations should judiciously evaluate their strategy in adopting deep learning.
Verdi March demystifies deep learning and shares his experience on how to gradually transition to deep learning. Using a specific example in computer vision, Verdi touches upon key differences in engineering traditional software versus deep learning-based software.
Verdi March is the chief research scientist with Deep Labs, specializing in parallel and distributed computing. Verdi has been applying his expertise to develop high-performance, scalable systems and applications across various domains and has extensive experience in the industrial R&D life-cycle at various Fortune 500 companies. Prior to joining Deep Labs, Verdi was a lead research scientist with Visa Labs, where he drove innovations on data science and next-generation big data platforms for payment analytics and risk managements. Previously, Verdi was with HP Labs Singapore, where he focused on cloud computing, and Sun Microsystems, where he focused on HPC/supercomputing. Verdi holds a PhD in computer science from the National University of Singapore and a bachelor of science in computer science from the University of Indonesia.
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