Announcing fast.ai diversity scholarships15 Aug 2017 Rachel Thomas
At fast.ai, we want to do our part to increase diversity in deep learning and to lower the unnecessary barriers to entry for everyone. Therefore, we are providing diversity scholarships for our updated in-person Practical Deep Learning for Coders course presented in conjunction with the University of San Francisco Data Institute, to be offered on Monday evenings in in downtown San Francisco and beginning on Oct 30. Wondering if you’re qualified? The only requirements are:
- At least 1 year of coding experience
- At least 8 hours a week to commit to the course (includes time for homework)
- Curiosity and a willingness to work hard
- Identify as a woman, person of Color, LGBTQ person, person with a disability, and/or veteran
- Be available to attend in-person 6:30-9pm, Monday evenings, in downtown San Francisco (SOMA)
Understanding the serious issues caused by homogenity issue in AI are important for us all, so read about the current state of the AI diversity crisis here.
The fast.ai approach
Fast.ai students have been accepted to the elite Google Brain residency, launched companies, won hackathons, invented a new fraud detection algorithm, had work featured on the HBO TV show Silicon Valley, and more, all from taking a course that has only one year of coding experience as the pre-requisite.
Last year we attempted an experiment: to see if we could teach deep learning to coders, with no math pre-requisites beyond high school math, and get them to state-of-the-art results in just 7 weeks. This was very different from other deep learning materials, many of which assume a graduate level math background, focus on theory, only work on toy problems, and don’t even include the practical tips. We didn’t even know if what we were attempting was possible, but the fast.ai course was a huge success!
The traditional approach to teaching math or deep learning requires that all the underlying components and theory be taught before learners can start creating and using models on their own. This approach to teaching is similar to not allowing children to play baseball until they have memorized all the formal rules and are able to commit to a full 9 innings with a full team, or to not allowing children to sing until they have extensive practice transcribing sheet music by hand in different keys. We want to get people “playing ball” (that is, applying deep learning to the problems they care about and getting great results) as quickly as possible, and we drill into the details later, as time goes on.
How to Apply
Women, people of Color, LGBTQ people, people with disabilities, and veterans in the Bay Area, if you have at least one year of coding experience and can commit 8 hours a week to working on the course, we encourage you to apply for a diversity scholarship. The number of scholarships we are able to offer depends on how much funding we receive. To apply, email email@example.com:
- title your email “Diversity Fellowship Application”
- include your resume
- 1 paragraph describing one or more problems you’d like to apply deep learning to
- confirm that you are available to attend the course on Monday evenings in SOMA (for 7 weeks, beginning Oct 30), and that you can commit 8 hours a week to working on the course
- which under-indexed group(s) you are a part of (gender, race, sexual identity, veteran)
The deadline to apply is Sept 15, 2017.
To those outside the Bay Area
We will again have a remote/international fellows program, which is separate from our diversity scholarships. Details on how to apply will be announced in a separate blog post in the next few weeks, so stay tuned.
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