Category Archives: Ml4all

ML Spends A Year In Burgundy with Jon Oropeza at ML4ALL

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.

The Talk

A Summary of ML Spends a Year in Burgundy

pic1

Jon starts his presentation journey with a childhood story. It involves computers and things, his childhood girlfriend, thumb wars, and other fascinating things. Give it a watch, get enthralled with a first step into machine learning for a professional web application developer, Jon.

pic2.pngJon introduces us via this story, to some of his first experiments with machine learning. He had a great adventure a while back with Burgundy and wine, and found lot’s of data on the wine of this and other sorts. He went to work finding out the best way to find how machines could determine a place and year through a liquid medium; wine. Getting into the wine varietals, the grapes, and other related criteria, where they’re born, and additional data Jon made great progress.

Going further he found ratings and other peripheral data and that he wanted even more, needed even more data, but found some interesting answers along the way. To see, hear, and learn more give his talk a viewing.

This is one of many talks from ML4ALL 2018, I’ll post more here with more reviews, with this being a taste of our aim with ML4ALL 2019. Join us, submit a proposal, or pick up a ticket today! It’s gonna be another create conference.

An Inspiring VLOG of @noopkat’s trip to Portland for ML4ALL

Last year amid the various blogs, conversations, writing, and other things I was doing attempting to put together a cool video of my ML4ALL experience my friend Suz (AKA @noopkat) posted a VLOG that summarized it better than I could put together, so I went about doing other things I could be productive at – like watching this VLOG she did.

 

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

IDE Launcher via Amtrak Cascades to Portland for ML4ALL

Got fidgety on the train, and just wanted to write code, on the way down to Portland for ML4ALL so I wrote up some decision tree code on determining what IDE’s I want opened up. Ya know, if you do something more than twice it needs automated, so I’ve started the process of automating all startup and shutdown tasks for a day’s coding. Simplistic geeky train geek code fun code is fun geeky train code. Cheers!

package main

import (
	"time"
	"fmt"
)

var sessionMinimal, sessionMedium, sessionLong, sessionZone time.Duration
var language string

func main() {
	sessionMinimal = 15
	sessionMedium = 45
	sessionLong = 90
	sessionZone = 180

	language = "golang"

	openIde("golang", 200)
}

func openIde(languageStack string, expectedCodingTime time.Duration) {
	var ide string

	switch  {
	case expectedCodingTime  sessionZone:
		ide = stackSpecific(languageStack, false, true, true)
		fmt.Printf("Launching: %s", ide)

	}
}

func stackSpecific(language string, fastLaunch bool, featureRich bool, introspective bool) string {
	if fastLaunch == true && featureRich == true && introspective == true {
		return "\n\nCome on, you know better. You get at best two out of three.\n\n"
	}

	if fastLaunch == true && featureRich == true {
		return "Visual Studio Code"
	}

	if featureRich == true && introspective == true {
		switch language {
		case "SQL":
			return "DataGrip"
		case "C":
			return "CLion"
		case "Python":
			return "PyCharm"
		case "golang":
			return "Goland"
		case "java":
			return "IntelliJ"
		case "scala":
			return "IntelliJ"
		case "kotlin":
			return "IntelliJ"
		case "dotnet":
			return "Rider"
		case "csharp":
			return "Rider"
		case "fsharp":
			return "Rider"
		case "vbnet":
			return "Rider"
		case "javascript":
			return "Webstorm"
		case "hcl":
			return "IntelliJ"
		case "ruby":
			return "RubyMine"
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "IntelliJ"
		}
	}

	if featureRich == true {
		switch language {
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "Visual Studio Code"
		}
	}

	if introspective == true {
		switch language {
		case "swift":
			return "AppCode"
		case "obj-c":
			return "AppCode"
		default:
			return "Visual Studio Code"
		}
	}

	if fastLaunch == true  {
		return "Sublime"
	}

	return "No IDE for you."
}

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

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.