Category: Unknown Code Ramblings

#ML4ALL Bike Ride Details

I previously posted the map when I introduced Igor and Carol, but for reference, here it is again with some additional details!

ML4ALL Ride
ML4ALL Ride

The ride will be what I’d call a “slow ride“, which is a super chill, easy going, roll through neighborhoods on the east side of Portland, down through the hip inner south east, and then back up around the waterfront. I’ll also provide a run down of a little Portland information about it’s wonky history, the neighborhood layout (check out point 2 on the map for instance, it’s called Ladd’s Addition), the awesome bridges the city has (two are unique in north America to Portland!), and more.

In addition we’ll also make a number of stops for photos, a coffee, and prospective a beer if the crew is up for it. We’ll leave at 2pm, and wrap up at 4pm in time to swing by our respective hotels and such before the evening reception. Our starting point is shown above, it’s a little hard to see but is denoted by a green dot! Basically we’re going to start at the Bossonova Ballroom Parking Lot and depart from there.

parking.png

BIKES @ Bike Town!

You may ask, but what if I don’t have a bike, I’m coming into town for this? Well, Portland has you covered! The easiest way is to pick up one of Portland’s many bike share bikes via Biketown. Which, to note, is FREE for May! To get a Biketown Bike just look for any of the orange bicycles, there’s a map on the Biketown Site too of all the stations where they’re parked, and when you download the mobile app you can see where any are nearby and easily reserve them and just go pick one up!

free

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).

Continue reading “Barriers To Accelerating The Training Of Artificial Neural Networks – A Systemic Perspective – Meet Manuel Muro” →

Cassandra: Quick Installation & Download Notes & Details

A collection of notes and details. There’s plenty of details and docs out there, which I’ll reference a few below to get started with Cassandra. The goal with these notes is to provide a kind of summarized punch list of items to quickly get started around Cassandra/Datastax Enterprise 6 (DSE6). I’ll have a number of additional pieces coming real soon, as I’ve got some geeky experiments and related surprise implementations I’m putting together in the very near future. Let’s just say there will likely be Lego, trains, and many nodes among other fascinating elements coming together to make it happen! Enjoy the start…

The Big Details

  • Who – Avinash Lakshman (@hedvigeng) and Prashant Malik (@pmalik) originally developed Cassandra at Facebook for inbox search.
  • 2000px-Cassandra_logo.svgWhat – Apache Cassandra is a highly scalable, high-performance distributed database built with the ability to handle large amounts of structured data decentralized across many servers. In service of that goal Cassandra provides a highly available system without a single point of failure.
  • Where – Apache Cassandra can be found on the Apache Cassandra Site and the code in the Cassandra Github Repo.
  • When – Cassandra was released as open source by Facebook in 2008, and became an Apache top-level project in February of 2010.
  • Why – When you want no fixed schema, massive scale, huge storage capability options, eventually consistent, fault-tolerant, dynamic/elastic scalability, fast linear-scale performance, always on highly available, and related features.

Distributed Databases

Getting Started, Installing, Configuration, Setup, & Start

CQL – Cassandra Query Language

Intro & Architecture of Cassandra References

This is merely the beginning of the blogging, projects, and notes, if you’d like to bookmark where I’ll be linking all of my Datastax + Cassandra notes, check out my Cassandra root documentation & links page.

 

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.