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


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