fast.ai is dedicated to making the power of deep learning accessible to all. We are convinced that deep learning will be a transformative technology that will dramatically improve medicine, education, agriculture, transport and many other fields, with the greatest impact in the developing world. But for this to happen, the technology needs to be much easier to use, more reliable, and more intuitive than it is today. We are working on hybrid “man and machine” solutions that harness the strengths of both humans and computers; building a library of ready-to-use applications and models; developing a complete educational framework; and writing fast and easy to use software for both developers and end users. More information is in our post: why we created fast.ai.
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 Distinguished Research Scientist at the University of San Francisco, a faculty member at Singularity University, and a Young Global Leader with the World Economic Forum.
Jeremy’s most recent startup, Enlitic, was the first company to apply deep learning to medicine, and has been selected one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was previously 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 has a math PhD from Duke and was selected by Forbes as one of “20 Incredible Women Advancing AI Research.” She is co-founder of fast.ai and a researcher-in-residence at the University of San Francisco Data Institute, where she teaches in the Masters in Analytics program. Her background includes working as a quant in energy trading, a data scientist + backend engineer at Uber, and a full-stack software instructor at Hackbright. Rachel’s writing has made the front page of Hacker News 4x; been translated into Chinese, Spanish, & Portuguese; and been featured in newsletters from O’Reilly, Fortune, Mattermark, & others. She writes an ask-a-data-scientist advice column at fast.ai and is on twitter @math_rachel.
Rachel co-founded fast.ai with the goal of making deep learning accessible to people from varied backgrounds outside of elite institutions, who are tackling problems in meaningful but low-resource areas, far from mainstream deep learning research. Over 50,000 students have started the FREE fast.ai course, Practical Deep Learning for Coders.
Rachel’s past projects include a computer model of irregular biochemical processes in children with Down Syndrome and Autism, Markov models comparing HIV treatments, Uber’s dynamic “surge” pricing algorithm, and a Monte Carlo simulator for an energy company with $25B yearly revenue. She has taught calculus at the college level, as well as leading intensive analysis reviews for incoming graduate students. At Hackbright Academy, she taught the full web development stack, as well as data structures and algorithms.
Some of Rachel’s most popular articles include: