Tag 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.

 

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

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!