blog.zakjost.comZak Jost

Home Blog GNN Course About Background Subscribe Light Dark Automatic Basics of Graph Neural Networks course Learn the basics of GNNs by joining my short (and free!) online course Join now ML Videos Check out the YouTube channel! WelcomeAIOverlords Join the Mailing List Get updates of new content releases Subscribe Support Become a Patreon Patreon Chat Join the discussion! Discord Connect Add me on LinkedIn LinkedIn Previous Next Posts * All Paper Review Deep Learning Tree Best Practices Cloud Noisy labels can wreak havoc Part 3 in the Fraud ML series Challenges of ML with PII data Part 2 in the Fraud ML series How to build better fraud detection systems with ML Part 1 of the Fraud ML series GuitarML An exploration of the intersection of ML and guitar circuit modeling Video Paper Code Discuss An Example of Graph Convolutional Networks Controlled experiments are run on the Citeseer citation graph to understand how GCNs work Code Paper Adversarial Validation How to use Adversarial Validation to diagnose overfitting problems Code Data Video Signal : Noise Dealing with variation in experimental results Noise Contrastive Estimation A Gentle Introduction Paper AdaNet - Adaptive Structural Learning of Artificial Neural Networks A paper review of Google's AdaNet AutoML technique, which learns the optimal neural network structure Code Paper Machine Learning Security Exploring the vulnerabilities of ML models from attack by bad actors. They're more fragile than you might think Review Paper Automated Hyper-parameter Optimization in SageMaker A tutorial in Python 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 Paper Code Overview of GANs - Part II Improving GANs: an introduction to DC-GAN Paper Overview of GANs - Part I An introduction to Generative Adversarial Networks Intro Video Math Deep Dive Paper Bayesian Additive Regression Trees A paper review of BART, which is the Bayesian approach to Additive Tree models Paper 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 Paper Applied Scientist @ AWS; Blogger; YouTuber Amazon Web Services Biography Hello! My name is Zak and I enjoy breaking seemingly complicated things down into simple ideas. I'm self-taught in the field of ML and started writing as a way of clarifying things that I learned, and this turned out to be an enjoyable enterprise that I've continued. I offer online courses on Graph Neural Networks and release content on YouTube and this blog. 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 an Applied Scientist at Amazon Web Services on the Graph Machine Learning team in the AI Research and Education org. Previously, I helped design and build Amazon Fraud Detector , which is a specialized AutoML solution that helps companies easily build ML-powered fraud detection systems. Prior to AWS I worked as a Data Scientist at Capital One and 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. Interests Machine Learning Education Education MS in Physics, 2011 University of Missouri - St. Louis BS in Physics, 2008 University of Missouri - St. Louis Experience Applied Scientist Amazon Web Services Mar 2017 – Present Seattle Working on the Graph ML team and building Neptune ML , which makes it easy for customers to do Machine Learning on their graph data stored in our graph database solution, Amazon Neptune. Previously, I helped design, build, and launch Amazon Fraud Detector , a fully managed service that makes it easy to build high-quality ML-powered fraud detection solutions. 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. Popular Topics Bayesian Methods Best Practices Cloud Deep Learning Fraud Generative Adversarial Networks Paper Review Production SageMaker Tree Powered by the Academic theme for Hugo . Cite × Copy...