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Name: Jackson Storm

Type: User

Bio: Physicist turned Data Scientist. Extensive programming, mathematics, analysis and model building experience. I also like things to look as good as they work.

Location: Sydney, Australia

Jackson Storm's Projects

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

hamiltorch

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

hotstepper

A Numpy based step function library for analysis and profit. More than just taking you up and down.

hotstepper-data

Data repository for hotstepper examples

hpobench

Collection of hyperparameter optimization benchmark problems

hydralit

A library to create multi-page Streamlit applications with ease.

hydralit-example

A sample project showing off the ease and ability of the Hydralit multi-page Streamlit package.

hydralit_components

A package of custom components for Streamlit and Hydralit. Can be used directly or in combination with the Hydralit package.

hydrogen

:atom: Run code interactively, inspect data, and plot. All the power of Jupyter kernels, inside your favorite text editor.

hyperimpute

A framework for prototyping and benchmarking imputation methods

hyperlearn

Waiting hours for a future prediction is unacceptable. Hyperlearn makes AI and ML algorithms 50% faster, use 90% less memory and doesn't require you to use new hardware! ML Algorithms like PCA, Linear Regression, NMF are all faster!