Foundations of GNNs course

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An exploration of the intersection of ML and guitar circuit modeling

An Example of Graph Convolutional Networks

Controlled experiments are run on the Citeseer citation graph to understand how GCNs work

Adversarial Validation

How to use Adversarial Validation to diagnose overfitting problems

Signal : Noise

Dealing with variation in experimental results

AdaNet - Adaptive Structural Learning of Artificial Neural Networks

A paper review of Google's AdaNet AutoML technique, which learns the optimal neural network structure

Machine Learning Security

Exploring the vulnerabilities of ML models from attack by bad actors. They're more fragile than you might think

Implementing Git in Data Science

Some thoughts on how to apply version control when conducting experiments

Committing to Sanity

Why git is critical for Data Science

More than Algorithms

Some important considerations for building and deploying models in industry

Sharing your SageMaker model

How to give access to your SageMaker models to another AWS account

Overview of GANs - Part III

Learning Disentangled Representations: an introduction to InfoGAN

Overview of GANs - Part II

Improving GANs: an introduction to DC-GAN

Overview of GANs - Part I

An introduction to Generative Adversarial Networks

Bayesian Additive Regression Trees

A paper review of BART, which is the Bayesian approach to Additive Tree models

Python for data analysis

A beginner's guide to data science packages in Python

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

A paper review on using Random Forests to estimate causal impact
Zak Jost

Zak Jost

ML Scientist @ AWS; Blogger; YouTuber

Amazon Web Services


Hello! My name is Zak and I think fulfillment in work is an important ingredient for a happy life. I have found Machine Learning to be an incredibly rewarding career path and I therefore want to help others that are on the same journey of self-teaching Machine Learning. Sometimes that journey was harder than it needed to be, so the point of this blog and my YouTube channel are to make it easier for others by simplifying concepts that seem complicated. And to help us all “find the others”, I am also working to build an online community. You can join us on Discord and keep up to date by subscribing to announcements here.

My day job is as a “Research Scientist” at Amazon Web Services, where I work with an amazing team. I helped design and build Amazon Fraud Detector, which is a specialized AutoML solution that helps companies easily build ML-powered fraud detection systems. I previously worked as a Data Scientist at Capital One, and prior to that in the semiconductor industry as a materials scientist.

I grew up in the rural town of Troy, Missouri, got my BS/MS degrees in Physics at the University of Missouri - St. Louis, and am married with two girls. We currently reside in West Seattle on beautiful Alki Beach.


  • Machine Learning
  • Entrepreneurship
  • Education


  • MS in Physics, 2011

    University of Missouri - St. Louis

  • BS in Physics, 2008

    University of Missouri - St. Louis





Jun 2018 – Present Seattle
Machine Learning consulting. Building end-to-end data products in the AdTech space.

Machine Learning Scientist

Amazon Web Services

Mar 2017 – Present Seattle
Helped design, build, and launch Amazon Fraud Detector, which is a fully managed service that makes it easy to build high-quality fraud detection ML models.

Principal Data Scientist

Capital One

Sep 2015 – Feb 2017 Dallas
Helped design and build a tech stack to replace the legacy SAS systems with Python-based workflows that automated and standardized model generation and reporting.

Research Scientist / Process Engineer

SunEdison Semiconductor

Jun 2011 – May 2015 St. Louis
Specialized in ion implantation of Helium and Hydrogen for transferring nanometer scale thin films on silicon wafers in the production of Silicon-on-Insulator materials.