1. Talk video from meetup April 11, 2017 at the AWS office in SF. Huge thanks to Amazon for providing venue, food/drink, and video recording!

    Title: How to Learn Deep Learning (when you’re not a computer science PhD)

    Speaker: Rachel Thomas (fast.ai)

    Abstract: Many people claim that deep learning needs to be a highly exclusive field, saying that you must spend years studying advanced math before you even begin to attempt it. Jeremy Howard and I believed that this was just not true, so we set out to see if we could teach deep learning to coders (with no math prerequisites) in 7 part-time weeks.

    Our students are now using deep learning to identify chainsaw noise in endangered rain forests, create translation resources for Pakistani languages, reduce farmer suicides in India, diagnose breast cancer, and more. We wanted to help them get results fast, so we taught them in a code-centric, application-focused way. I’ll share what we learnt about how to learn deep learning effectively, so that you can set out on your own learning journey.

    Bio: Rachel Thomas has a math PhD from Duke and previously worked as a quant, a data scientist at Uber, and a full-stack software instructor at Hackbright. She has made the front page of Hacker News 4x, including for her posts If you think women in tech is just a pipeline issue, you haven’t been paying attention and Deep Learning: Not Just for Silicon Valley, and also writes an ask-a-data-scientist advice column. She 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.

    # vimeo.com/214233053 Uploaded 6,276 Plays 0 Comments
  2. "One person, in a literal garage, building a self-driving car." That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.

    Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple "A.I. winters"? What's the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?

    From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.

    # vimeo.com/170189199 Uploaded

Deep Learning

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