Tag Archives: conference

Art, JavaScript, and Machine Learning with Amy Cheng at ML4ALL 2018

One talk that opened my mind to new ideas about where, how, and when to use machine learning was Amy Cheng’s @am3thyst talk on Machine Learning, Art, and JavaScript. I introduced her last year in my previous post, and am linking the talk below. Give it a watch, it’s worth the listen!

ML4ALL 2019 is on. CFP is still open, a little longer. It’s closing on the 18th. In addition we’ve got tickets available for early birds, but those will be gone soon too so pick one up while you can, it’s only a $200.00 bucks. You’ll basically be getting a ticket to conference that’ll be 10x the value of one of these big corporate conferences for $200 bucks, in the awesome city of Portland, and I can promise you it’ll be a conference you’ll get more out of then you currently think you will! Join us, it’s going to be a great time!

It’s Official, ML4ALL 2019, Machine Learning Conference 4 All v2!

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 ml4allconf@gmail.com 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!

The TLDR;

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!

 

ML4ALL LiveStream, Talks & More

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”

-> Introduction of Manuel

Jon Oropeza “ML Spends A Year In Burgundy”

-> Introduction of Jon

Igor Dziuba “Teach Machine To Teach: Personal Tutor For Language Learners”

-> Introduction to Igor

Barriers To Accelerating The Training Of Artificial Neural Networks – A Systemic Perspective – Meet Manuel Muro

manuel-muroThe 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).

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Conducting a Data Science Contest in Your Organization w/ Ashutosh Sanzgiri

ashutosh-sanzgiriAshutosh 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!

From Zero to Machine Learning for Everyone w/ Poul Peterson

poul-petersenBigML was founded in January 2011 in Corvallis, Oregon with the mission of making Machine Learning beautifully simple for everyone. We pioneered Machine Learning as a Service (MLaaS), creating our platform that effectively lowers the barriers of entry to help organizations of all industries and sizes to adopt Machine Learning.

As a local company with a mission in complete alignment with that of the conference, BigML would be delighted to partake in this first edition of ML4ALL.

“So you’ve heard of Machine Learning and are eager to make data driven decisions, but don’t know where to start? The first step is not to read all the latest and greatest research in artificial intelligence, but rather to focus on the data you have and the decisions you want to make. Fortunately, this is easier than ever because platforms like BigML have commoditized Machine Learning, providing a consistent abstraction making it simple to solve use cases across industries, no Ph.D.s required.

As a practical, jump-start into Machine Learning, Poul Petersen, CIO of BigML, will demonstrate how to build a housing recommender system. In just 30 minutes, he will cover a blend of foundational Machine Learning techniques like classification, anomaly detection, clustering, association discovery and topic modeling to create an end-to-end predictive application. More importantly, using the availability of an API will make it easy to put this model into production, on stage, complete with a voice interface and a GUI. Learn Machine Learning and find a great home – all without paying expensive experts!”

Poul Petersen (@pejpgrep) is the Chief Infrastructure Officer at BigML. An Oregon native, he has an MS degree in Mathematics as well as BS degrees in Mathematics, Physics and Engineering Physics from Oregon State University. With 20 plus years of experience building scalable and fault-tolerant systems in data centers, Poul currently enjoys the benefits of programmatic infrastructure, hacking in python to run BigML with only a laptop and a cloud.

 

Your First NLP Machine Learning Project: Perks and Pitfalls of Unstructured Data w/ Anna Widiger

anna-widigerAnna Widiger (@widiger_anna) has a B.A. degree in Computational Linguistics from University of Tübingen. She’s been doing NLP since her very first programming assignment, specializing in Russian morphology, German syntax, cross-lingual named entity recognition, topic modeling, and grammatical error detection.

Anna describes “Your First NLP Machine Learning Project: Perks and Pitfalls of Unstructured Data” to us. Faced with words instead of numbers, many data scientists prefer to feed words straight from csv files into lists without filtering or transformation, but there is a better way! Text normalization improves the quality of your data for future analysis and increases the accuracy of your machine learning model.

Which text preprocessing steps are necessary and which ones are “nice-to-have” depends on the source of your data and the information you want to extract from it. It’s important to know what goes into the bag of words and what metrics are useful to compare word frequencies in documents. In this hands-on talk, I will show some do’s and don’ts for processing tweets, Yelp reviews, and multilingual news articles using spaCy.