Deep Learning Diversity Fellowship Applications Now Open

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Cutting Edge Deep Learning for Coders (Part 2) will be taught this spring at the USF Data Institute in downtown San Francisco, on Monday evenings from March 19 to April 30. This course builds on our updated Practical Deep Learning for Coders (Part 1). The preview version of Part 1 is available here and the final version will be released in the next week.

We are now accepting applications from women, people of Color, LGBTQ people, and veterans for diversity scholarships for the deep learning part 2 course. The prerequisites are:

You can fulfill the requirement to be familiar with deep learning, the fastai library, and PyTorch by doing any 1 of the following:

Preference will be given to students that have already completed some machine learning or deep learning education, including any courses, the Coursera machine learning course,, or Stanford’s CS231n.

Deep Learning Part 1 covers the use of deep learning for image recognition, recommendation systems, sentiment analysis, and time-series prediction. Part 2 will take this further by teaching you how to read and implement cutting edge research papers, generative models and other advanced architectures, and more in-depth natural language processing. As with all courses, it will be practical, state-of-the-art, and geared towards coders.

Increasing diversity in AI is a core part of our mission at to make deep learning more accessible. 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. We are horrified by unethical uses of AI, widespread bias, and how overwhelmingly white and male most deep learning teams are. Increasing diversity won’t solve the problem of ethics and bias alone, but it is a necessary step.

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, can fulfill the deep learning pre-requisite (described above), 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 (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:

1 paragraph describing one or more problems you’d like to apply deep learning to 1 paragraph describing previous machine learning education (e.g. courses, coursera,,…) Confirm that you fulfill the deep learning part 1 pre-requisite (or that you have already completed the first 2 lessons and plan to complete the rest before the course starts) Confirm that you are available to attend the course on Monday evenings in SOMA (for 7 weeks, beginning March 19), 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)

Diversity Fellowship applications should be submitted here:

If you have any questions, please email

The deadline to apply is January 24, 2018.


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 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 Deep Learning part 2 course taught in spring 2017? Our goal at 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. Also, last year’s course was taught primarily in TensorFlow, while this was in in PyTorch.

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