## Solving the mystery of my dog's breed with ML

Using the Stanford Dogs Dataset, deep learning, and explainability through prototypes to infer the unknown breed of my dog

## Training models when data doesn't fit in memory

Using Dask and some other tricks so you can train your models under memory constraints

## The Bayesian Bootstrap

Faster, smoother version of the bootstrap that yields better results on small data

## Forest Embeddings Counterfactual

Another tree-based causal inference model using nearest neighbors on the embedding produced by a Random Forest

## Decision Tree Counterfactual

Decision Trees can be decent causal inference models, with a few tweaks

## Risk and Uncertainty in Deep Learning

Building a neural network that can estimate aleatoric and epistemic uncertainty at the same time

## A Practical Introduction to Randomized Prior Functions

Understanding a state-of-the-art bayesian deep learning method with Keras code

## A Causal Look At What Makes a Kaggler Valuable

Using causal inference to determine what titles and skills will make you earn more

## Causal inference and treatment effect estimation

Estimating counterfactual outcomes using Generalized Random Forests, ExtraTrees and embeddings

## Calibration of probabilities for tree-based models

Calibrating probabilities of Random Forests and Gradient Boosting Machines with no loss of performance with a stacked logistic regression

## Supervised dimensionality reduction and clustering at scale with RFs with UMAP

Uncovering relevant structure and visualizing it at scale by partnering Extremely Randomized Trees and UMAP

## Risk-aware bandits

Experimenting with risk-aware reward signals, Thompson Sampling, Bayesian UCB, and the MaRaB algorithm

## Approximate bayesian inference for bandits

Experimenting with Conjugate Priors, MCMC Sampling, Variational Inference and Bootstrapping to solve a Gaussian Bandit problem

## Non-stationary bandits

Solving a Bernoulli Multi-Armed Bandit problem where reward probabilities change over time

## Supervised clustering and forest embeddings

Using forests of randomized trees to uncover structure that really matters in messy data

## Bootstrapped Neural Networks, RFs, and the Mushroom bandit

Bootstrapped Neural Networks and Random Forests for solving a more realistic contextual bandit problem

## Thompson Sampling, GPs, and Bayesian Optimization

Mixing Thompson Sampling and Gaussian Processes to optimize non-convex and non-differentiable objective functions

## Thompson Sampling for Contextual bandits

Solving a Contextual bandit problem with Bayesian Logistic Regression and Thompson Sampling

## Introduction to Thompson Sampling: the Bernoulli bandit

Introducing Thompson Sampling and comparing it to the Upper Confidence Bound and epsilon-greedy strategies in a simple problem