Deep learning is transforming the world. We are making deep learning easier to use and getting more people from all backgrounds involved through our:
The world needs everyone involved with AI, no matter how unlikely your background.
Being cool is about being exclusive, and that’s the opposite of what we want. We want to make deep learning as accessible as possible– including to people using uncool languages like C#, uncool operating systems like Windows (which is used by the majority of the world), uncool datasets (way smaller than anything at Google, and in domain areas you’d consider obscure), and with uncool backgrounds (maybe you didn’t go to Stanford).
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.
Rachel Thomas is a professor at the University of San Francisco Data Institute and co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 200,000 students have taken and which has been featured in The Economist, MIT Tech Review, and Forbes. She was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. Rachel is a popular writer and keynote speaker. In her TEDx talk, she shares what scares her about AI and why we need people from all backgrounds involved with AI.
Rachel’s writing has been read by nearly a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 9x. Some of her most popular articles include:
Rachel’s talks include:
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.