Sponsor a Deep Learning Diversity Scholarship17 Aug 2017 Rachel Thomas
Last year’s diversity fellowships (funded by University of San Francisco and fast.ai), open to women, people of Color, LGBTQ people, and vets, played a role in helping us create a diverse community. However, we need your help to be able to offer additional scholarships this year. If your company, firm, or organization would be willing to sponsor diversity scholarships ($1,500 each), please email email@example.com.
Deep learning is incredibly powerful and is being used to diagnose cancer, stop deforestation of endangered rainforests, provide better crop insurance to farmers in India, provide better language translation than humans, improve energy efficiency, and more. To find out why diversity in AI is a crucial issue, read this post on the AI diversity crisis.
While many in tech are bemoaning the “skills gap” or “talent shortage” in trying to hire AI practitioners, we at fast.ai set out 1 year ago with a novel experiment: could we teach deep learning to coders with no pre-requisites beyond just 1 year of coding experience? Other deep learning materials often assume an advanced math background, yet we were able to get our students to the state of the art, through a practical, hands-on approach in our part-time course, without the advanced math requirements. Our students have been incredibly successful and their stories include the following:
- Sara Hooker, who only started coding 2 years ago, and is now part of the elite Google Brain Residency
- Tim Anglade, who used Tensorflow to create the Not Hot Dog app for HBO’s Silicon Valley, leading Google’s CEO to tweet “our work here is done”
- Gleb Esman, who created a new fraud product for Splunk using the tools he learnt in the course, and was featured on Splunk’s blog
- Jacques Mattheij, who built a robotic system to sort two tons of lego
- Karthik Kannan, founder of letsenvision.com, who told us “Today I’ve picked up steam enough to confidently work on my own CV startup and the seed for it was sowed by fast.ai with Pt1. and Pt.2”
- Matthew Kleinsmith and Brendon Fortuner, who in 24 hours built a system to add filters to the background and foreground of videos, giving them victory in the 2017 Deep Learning Hackathon.
For those interested in applying for our diversity fellowships (to take our course), read this post for details.
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