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EE380 Computer Systems Colloquium presents "fastai: A Layered API for Deep Learning"

Topic: 
fastai: A Layered API for Deep Learning
Wednesday, February 19, 2020 - 4:30pm
Venue: 
Shriram 104
Speaker: 
Jeremy Howard (fastai; USF) and Sylvain Gugger (fastai)
Abstract / Description: 


[speaker photo] Sylvain's research at fast.ai has focused on designing and improving techniques that allow models to train fast with limited resources. He has also been a core developer of the fastai library, including implementing the warping transformations, the preprocessing pipeline, much of fastai.text, and a lot more.
Prior to fastai, Sylvain was a Mathematics and Computer Science teacher in Paris for seven years. He taught CPGE, the 2-year French program that prepares students for graduate programs at France's top engineering schools (the "grandes écoles"). After relocating to the USA in 2015, Sylvain wrote ten textbooks covering the entire CPGE curriculum. Sylvain is an alumni from École Normale Supérieure (Paris, France) and has a Master's Degree in Mathematics from University Paris XI (Orsay, France). He lives in Brooklyn with his husband and two sons.fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes: a new type dispatch system for Python along with a semantic type hierarchy for tensors; a GPU-optimized computer vision library which can be extended in pure Python; an optimizer which refactors out the common functionality of modern optimizers into two basic pieces, allowing optimization algorithms to be implemented in 4-5 lines of code; a novel 2-way callback system that can access any part of the data, model, or optimizer and change it at any point during training; a new data block API; and much more. We have used this library to successfully create a complete deep learning course, which we were able to write more quickly than using previous approaches, and the code was more clear. The library is already in wide use in research, industry, and teaching.

Bios:

[speaker photo]Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a faculty member at the University of San Francisco, and is Chief Scientist at doc.ai and platform.ai.

Previously, Jeremy was the founding CEO Enlitic, which was the first company to apply deep learning to medicine, and was selected as one of the world's top 50 smartest companies by MIT Tech Review two years running. He was the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group--purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.

He has many television and other video appearances, including as a regular guest on Australia's highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.

[speaker photo]Sylvain Gugger's research at fast.ai has focused on designing and improving techniques that allow models to train fast with limited resources. He has also been a core developer of the fastai library, including implementing the warping transformations, the preprocessing pipeline, much of fastai.text, and a lot more.

Prior to fastai, Sylvain was a Mathematics and Computer Science teacher in Paris for seven years. He taught CPGE, the 2-year French program that prepares students for graduate programs at France's top engineering schools (the "grandes écoles"). After relocating to the USA in 2015, Sylvain wrote ten textbooks covering the entire CPGE curriculum. Sylvain is an alumni from École Normale Supérieure (Paris, France) and has a Master's Degree in Mathematics from University Paris XI (Orsay, France). He lives in Brooklyn with his husband and two sons.