Practical Deep Learning for Coders, part-time Diversity Fellowships, Fall 2018

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. We are providing diversity scholarships for our updated part-time, in-person Practical Deep Learning for Coders course presented in conjunction with the University of San Francisco Data Institute, to be offered one evening per week for 7 weeks, starting October 22, in downtown San Francisco. Women, people of Color, LGBTQ people, people with disabilities, and/or veterans are eligible to apply.

The deadline to apply is September 17, 2018. Details on how to apply, and FAQ, are at the end of this post.

The Power of Deep Learning

Deep learning has great potential for good. It is being used by fast.ai students and teachers to diagnose cancer, stop deforestation of endangered rain-forests, provide better crop insurance to farmers in India (who otherwise have to take predatory loans from thugs, which have led to high suicide rates), help Urdu speakers in Pakistan, develop wearable devices for patients with Parkinson’s disease, and much more. Deep learning could address the global shortage of doctors, provide more accurate medical diagnoses, improve energy efficiency, increase farm yields, and reduce pesticide use.

However, there is also great potential for harm. We are worried about unethical uses of data science, and about the ways that society’s racial and gender biases (summary here) are being encoded into our machine learning systems. We are concerned that an extremely homogeneous group is building technology that impacts everyone. People can’t address problems that they’re not aware of, and with more diverse practitioners, a wider variety of important societal problems will be tackled.

We want to get deep learning into the hands of as many people as possible, from as many diverse backgrounds as possible. People with different backgrounds have different problems they’re interested in solving. The traditional approach is to start with an AI expert and then give them a problem to work on; at fast.ai we want people who are knowledgeable and passionate about the problems they are working on, and we’ll teach them the deep learning they need.

While some people worry that it’s risky for more people to have access to AI; I believe the opposite. We’ve already seen the harm wreaked by elite and exclusive companies such as Facebook, Palantir, and YouTube/Google. Getting people from a wider range of backgrounds involved can help us address these problems.

The fast.ai approach

We began fast.ai with 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 has been a huge success!

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.

Coverage of fast.ai in the Verge and MIT Technology Review
Coverage of fast.ai in the Verge and MIT Technology Review

fast.ai is not just an educational resource; we also do cutting-edge research and have achieved state-of-the-art results. Our wins (and here) in Stanford’s DAWNBench competition against much better funded teams from Google and Intel were covered in the MIT Tech Review and the Verge. Jeremy’s work with Sebastian Ruder achieving state-of-the art on 6 language classification datasets was accepted by ACL and is being built upon by OpenAI. All this research is incorporated into our course, teaching students state-of-the-art techniques.

How to Apply

Deep Learning Part 1 covers the use of deep learning for image recognition, recommendation systems, sentiment analysis, and time-series prediction. Wondering if you’re qualified? The only requirements are:

The number of scholarships we are able to offer depends on how much funding we receive (if your organization may be able to sponsor one or more places, please let us know). To apply for the fellowship, you will need to submit a resume and statement of purpose. The statement of purpose will include the following:

Diversity Fellowship applications should be submitted here: https://gradapply.usfca.edu/register/di_certificates

If you have any questions, please email datainstitute@usfca.edu.

The deadline to apply is September 17, 2018.

FAQ

I’m not eligible for the diversity scholarship, but I’m still interested. Can I take the course? Absolutely! You can register here.

I don’t live in the San Francisco Bay Area; can I participate remotely? Yes! Once again, we will be offering remote international fellowships. Stay tuned for details to be released in a blog post in the next few weeks.

Will this course be made available online later? Yes, this course will be made freely available online afterwards. Benefits of taking the in-person course include earlier access, community and in-person interaction, and more structure (for those that struggle with motivation when taking online courses).

Is fast.ai able to sponsor visas or provide stipends for living expenses? No, we are not able to sponsor visas nor to cover living expenses.

How will this course differ from the previous fast.ai courses? Our goal at fast.ai is to push the state-of-the-art. Each year, we want to make deep learning increasingly intuitive to use while giving better results. With our fastai library, we are beating our own state-of-the-art results from last year. This year’s course will coincide with our release of an updated version of the fastai library (built on top of PyTorch).

What language is the course taught in? The course is taught in Python, using the fastai library and PyTorch. Some of our students have gone on to use the fastai library in production at Fortune 500 companies.