Month: April 2019

Metal Monday for Week of April 8th

Good morning, here’s your new dosage of Monday morning thrash! This band, Nervosa runs the thrashing genre into new lines of perfection. Maybe, “porra incrível surra de metal” says it better, but then of course maybe it doesn’t since I’m only but so good with linguistics.

nervosaband2018

This first song, a routine feeling I get, is a need to kill the silence. Who knows what it is, but I’d prefer the chaos of the city, or the prey of timid realms of the forest, than the tranquility of pure silent nothingness.

 

Next band up to light the morning off. So a pirate, a chemical warfare toxic ninja, a dirty metal head, a mechanic, and a demonic princess walk onto stage.

follow-the-cipher

No, it isn’t the start of a joke. Follow the Cipher brings a different mix of things together for a rather entertaining oddity of a show.

Last of the trio, one of the most brutal bands I’ve heard as of late. Truly bringing the dark orb to show is Spoil Engine.

spoil-engine

The song Disconnect really draws me in regarding today’s political matricide of Terra, and the horrifyingly disgusting myopia of continued negligence, obliviousness, and disregard for the very place we have origin and exist today. Not that the song is specifically about Terra, but about the media control and our allowance as a people of its control over our collective ongoing conversation.

Enjoy that thrashing code!

Siyu Yang Presenting “Deep Learning for Computer Vision with Applications for Wildlife Conservation”

siyu-yangIntroducing Siyu Yang > @yangsiyu_ < presenting “Deep Learning for Computer Vision with Applications for Wildlife Conservation”.

Siyu is a data scientist at the AI for Earth program at Microsoft focusing on developing machine learning enabled solutions to aid environmental conservation efforts at university labs, startups, non-profits, and government agencies. Before joining this team, she worked on data powered code completion tools at Visual Studio.

At the AI for Earth program at Microsoft, we find new ways to use machine learning to solve global environmental challenges. This talk is an overview of several problems in wildlife conservation where computer vision is poised to make a significant impact. We will survey three different problems in this space: species classification from handheld photos, automated camera trap image processing, and animal detection from aerial imagery. For each area, we will discuss (1) why this problem is challenging from a computer vision perspective, (2) why solving this problem can support conservation, and (3) the data sets we’re working with and the models we’ve built to address this problem. The talk will also include a demo of the AI for Earth Species Classification API and the Camera Trap Animal Detection API.

What will attendees learn:

  1. Find out what Microsoft’s AI for Earth program is all about
  2. Understand how data, particularly visual data, is used by wildlife biologists for conservation.
  3. Experience through live demos how computer vision techniques can bring value to people working with such data, and understand our plan to deploy these technologies.

Come check out Siyu Yang’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Amy Hodler Presenting “Improving ML Predictions with Connected Feature Extraction”

UPDATED: Video Added from the Conference!

Introducing Amy Hodler > @amyhodler < presenting “Improving ML Predictions with Connected Feature Extraction”.

amy-hodlerAmy is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory.

One of the most practical ways to improve our machine learning predictions right away is by using graphs for connected features. You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting.

In this session, you’ll hear about a link prediction example for collaboration with tips on training and evaluating a model using Neo4j and Spark. We’ll compare several models and show measurable improvements in accuracy, precision, and recall by folding in graph-based features.

Come check out Amy Hodler’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Melissa Santos Presenting “YOU Can Predict the Future?”

UPDATED: Video Added from the Conference!

Introducing Melissa Santos > @ansate < presenting “YOU Can Predict the Future?”.

melissa-santosMelissa has been working with computers and data since 2000, in fields from security to marketing to geography. She has a PhD. in Applied Math and considers herself both a statistician and a data scientist. Currently she is a data analyst at Pingboard, helping understand the customers and how they use the product.

Let’s get started with forecasting! We’ll look at simple linear trends, polynomial fits, ARIMA models, and using Facebook’s Prophet library. The goal is to introduce you to forecasting and help you try modeling the future. We will also look at goodness-of-fit measures so we can compare our models.

Come check out Melissa Santos’ talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!

Damon Danieli Presenting “Time-Series Behavioral Analysis for Churn Prediction”

UPDATED: Video Added from the Conference!

Introducing Damon Danieli > @damondanieli < presenting “Time-Series Behavioral Analysis for Churn Prediction“.

damon-danieliVice President of Engineering for Platform Architecture at DocuSign. I am responsible for the internal advanced analytics data platform as well as applied machine learning for both internal efficiency for sales, marketing and customer success and external intelligent features. Big things are coming!

Previously…

Founder and CTO of Appuri [Acquired by DocuSign in December 2017]. Appuri is a Customer Data Platform that enables Software as a Service (SaaS) businesses to maximize their retention, engagement and revenue. Appuri captures and connects behavioral data from your website and mobile applications and combines it with other sources such as transactional data and 3rd party demographic augmentation.

This talk covers the data engineering, feature extraction, and the processing for predicting future user behavior. By the end of the presentation, you should have a direction to explore if you are building your own system as well as some concrete patterns that we found worked for us.

We will use real-world examples for B2B SaaS churn prediction, but this talk is equally applicable to predicting any type of outcome that is correlated with user behavior such as conversion to a paying customer, upsell to additional products, etc.

We will present lessons learned from several iterations of productizing a system that can take product usage user telemetry events (from Mixpanel, Amplitude, Heap, homegrown, etc) and combine that with business objects (Salesforce) and the application database. We will include some mistakes we’ve made along the way.

Come check out Damon Danieli’s talk at ML4ALL happening April 28th-30th in amazing Portland, Oregon! Get your tickets to attend here. For the schedule, our excellent sponsors docs for the conference, check out the ML4ALL Conference Site!