Say Hello to Jon and Amy @ML4ALL!

Welcome the next two speakers I’m introducing: Jon Oropeza and Amy Cheng!

ML Spends A Year In Burgundy

Jon (@joropeza) is coming to Portland from Portland to speak to us about Cote d’Or in Burgundy! He’s a hacker, grape lover, and Portlander that loves a good smooth wine made out of those grapes he loves!

Jon being a both a weather nerd and a wine nerd, he was curious if machine learning could be applied to vintages in an area where a) quality of wine varies greatly by year b) most of that variance has to do with weather patterns and different aspects of temperature and precipitation c) there are known, reasonably-objective classifications or ‘scores’ of each vintage, such that we could say that such-and-such year with such-and-such weather produced wines of good/bad/mediocre quality.

With that in mind, Jon pulled down a ton of weather data for the Cote d’Or region in Burgundy, France, prepped it, ran a few supervised models over it, chose the one that seemed to work best, and arrived at something that he thinks might be really cool to share with people. I’m betting it’ll be pretty freakin’ rad! With the conference being in late May, he’s even noted we’ll be able to look at some insights into whether 2018 is shaping up to be a good, great, or terrible year! 🙂

Machine Learning, Art and JavaScript

Amy Cheng (@am3thyst) is going to arrive in Portland from the small hip village of Brooklyn ready to impart some knowledge about JavaScript with some art among it all! She’s a full-stack web dev who likes to explore the creative and expressive possibilities of code. She’s intrigued by machine learning because of all the questions about art it inspires. Such as, what does art mean in an age when computers can create new work based off old masters? Can a computer learn to be creative or can it only propagate existing human biases and tastes? Is there such a thing as neural aesthetics?

Machine learning can seem like an inaccessible subject, filled with complicated mathematical algorithms. In this talk, Amy will show that you don’t need a high-level degree in computer science or access to a cluster of supercomputers in order to experiment with machine learning. All you need is JavaScript and the web browser.

Amy will teach us the core concepts of machine learning by observing how a computer makes art, specifically how style transfer works. How does a computer learn to look at the world and then recreate what it sees? How does a computer paint like Van Gogh? Using available JavaScript toolkits, libraries, and APIs, she’ll show how creating neural networks in code can lead a computer to paint like Van Gogh.

I’m super stoked to find out what, and see these two present at ML4ALL! Maybe they’ll even join the bike ride I’ll lead and enjoy a bit of Portland on Sunday before the conference kicks off! To checkout ML4ALL and get your tickets navigate over to ML4ALL here!

• Manuel -> Barriers To Accelerating The Training Of Artificial Neural Networks – A Systemic Perspective
• Ashutosh -> Conducting a Data Science Contest in Your Organization
• Poul -> From Zero to Machine Learning for Everyone
• Anna -> Your First NLP Machine Learning Project: Perks and Pitfalls of Unstructured Data
• Kaleo Ha’o -> Jump or Not to Jump: Solving Flappy Bird with Deep Reinforcement Learning
• Mary & Robert -> Eliminating Machine Bias & Getting from Machine Learning Outputs to Decisions… timely things!
• Kirsten & Rich -> Now, Kirsten Westeinde & Rich Jolly
• Jon & Amy -> Say Hello to Jon and Amy @ML4ALL!
• Igor & Carol -> More @ML4ALL Intros: Meet Igor & Carol! Also, the bike ride map!
• Clair & Ricky -> @ML4ALL Meet Clair J. Sullivan & Ricky Hennessy
• Paige & Suz -> ML4ALL Speakers – Meet Paige & Suz