The high-level view of deep learning is elegant: composing differentiable components together trained in an end-to-end fashion. The reality isn’t that simple, and the commonly used tools greatly limit what we are capable of doing. Diogo Almeida explains what we can do about it and offers a practical attempt at a deep learning library of the future.
Diogo Moitinho de Almeida is a data scientist, software engineer, and hacker. Currently, Diogo is a senior data scientist at Enlitic, where he works to radically improve the quality of medical diagnosis using deep learning, advance the state of the art in modeling, and build novel ways to interact with neural networks. Previously, he was a medalist at the International Math Olympiad, ending a 13-year losing streak for the Philippines; received the top prize in the Interdisciplinary Contest in Modeling, achieving the highest distinction of any team from the Western Hemisphere; and won a Kaggle competition, setting a new state of the art for black box identification of causality and getting the opportunity to speak at the Conference on Neural Information Processing Systems.
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