We’re building up to ML4ALL 2019, and in the meanwhile I want to re-introduce some of the past speakers and show you their talks. This first, of the many, is Jon Oropeza. I introduced him last year here, so check out his talk and work, he’s got a lot of good stuff he’s put together!.
It’s official, we’ve got dates and tickets are open for ML4ALL 2019! Our CFP will be open in a number of hours, not days, and I’ll do another update the second that we have that live.
What is ML4ALL?
ML4ALL stands for “Machine Learning for All“. Last year I enjoyed working with Alena Hall, Troy Howard, Glenn Block, Byron Gerlach, and Ben Acker on getting a great conference put together, and I’m looking forward to rounding up a team and doing a great job putting together another great conference for the community again this year!
Last year @lenadroid put together this great video of the event and some short interviews with speakers and attendees. It’s a solid watch, take a few minutes and check it out for a good idea of what the conference will be like.
Want to Attend? Help!
Tickets are on sale, but there’s a lot of other ways to get involved now. First, the super easy way to keep track of updates is to follow the Twitter account: @ml4all. The second way is a little bit more involved, but can be a much higher return on investment for you, by joining the ML4ALL Slack Group! There we discuss conference updates, talk about machine learning, introduce ourselves, and a range of other discussions.
If you work for a company in the machine learning domain, plying the wave of artificial intelligence and related business, you may want to get involved by sponsoring the conference. We’ve got a prospectus we can send you for the varying levels, just send an email to firstname.lastname@example.org with the subject “Plz Prospectus”. We’ll send you the prospectus and we can start a conversation on which level works best for your company!
ML4ALL is a conference that will cover from beginner to advanced machine learning presentations, conversations, and community discussions. It’s a top conference choice to put on your schedule for April 28-30th, pick up tickets for, and submit a proposal to the CFP!
If you’re attending, or if you’re at the office or at home, you can check out the talks as they go live on the ML4ALL Youtube Channel! Right now during the conference we also have the live feed on the channel, so if you’re feeling a little FOMO this might help a little. Enjoy!
Here are a few gems that are live already!
Manuel Muro “Barriers To Accelerating The Training Of Artificial Neural Networks”
The real breakthrough for the modern Artificial Intelligence (AI) and Machine Learning (ML) technology explosions started back in 1943 when researchers McCulloch & Pitts came up with a mathematical model to represent that function of the biological neuron; nature’s gift that allows all life to operate and learn over time. Eventually this research would then give birth to the Artificial Neural Network (ANN).
Ashutosh Sanzgiri (@sanzgiri) is a Data Scientist at AppNexus, the world’s largest independent Online Advertising (Ad Tech) company. I develop algorithms for machine learning products and services that help digital publishers optimize the monetization of their inventory on the AppNexus platform.
Ashutosh has a diverse educational and career background. He’s attained a Bachelor’s degree in Engineering Physics from the Indian Institute of Technology, Mumbai, a Ph.D. in Particle Physics from Texas A&M University and he’s conducted Post-Doctoral research in Nuclear Physics at Yale University. In addition to these achievements Ashutosh also has a certificate in Computational Finance and an MBA from the Oregon Health & Sciences University.
Prior to joining AppNexus, Ashutosh has held positions in Embedded Software Development, Agile Project Management, Program Management and Technical Leadership at Tektronix, Xerox, Grass Valley and Nike.
Scaling Machine Learning (ML) at your organization means increasing ML knowledge beyond the Data Science (DS) department. Several companies have Data / ML literacy strategies in place, usually through an internal data science university or a formal training program. At AppNexus, we’ve been experimenting with different ways to expand the use of ML in our products and services and share responsibility for its evaluation. An internal contest adds a competitive element, and makes the learning process more fun. It can engage people to work on a problem that’s important to the company instead of working on generic examples (e.g. “cat vs dog” classification), and gives contestants familiarity with the tools used by the DS team.
In this talk, Ashutosh will present the experience of conducting a “Kaggle-style” internal DS contest at AppNexus. he’ll discuss our motivations for doing it and how we went about it. Then he’ll share the tools we developed to host the contest. The hope being you too will find inspiration to try something in your organization!
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