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11ty-landing-page

algorithms-tour

How data science is woven into the fabric of Stitch Fix

amazon-dsstne

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

config

dot files and other configuration files

constantine

Constantine helps you find your posts in the Hellthread.

contributing-template

Template for writing your own contributing guide

datasciencecoursera

Creating my first repository

dfp_wthh_data

analyzing midterm election data

hydroponics

implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

introduction-to-python

A collection of tools that I use to introduce summer high school students to python and jupyter notebooks before they begin as research assistants.

loseit

Analyzing previous weight loss data from r/loseit weight loss challenges

machine-learning-asset-management

Machine Learning in Asset Management

market-basket

my_portfolio

My personal website that contains projects that I have worked on and publications from my PhD research.

n2v

This is the implementation of Noise2Void training.

nx_altair

Draw NetworkX graphs with Altair

openrectorch

My fork of OpenRec, an open-source and modular library for neural network-inspired recommendation algorithms.

poincare-embeddings

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"

pytools

Useful functions for Python written in either C, Cython, or Python.

pytorch_sphinx_theme

PyTorch Sphinx Theme

reactive-stock-trader

A Stock Trader system to demonstrate reactive systems development

recsys2018-evaluation-tutorial

sars_tutorial

Repository for the tutorial on Sequence-Aware Recommender Systems held at TheWebConf 2019 and ACM RecSys 2018

simple_mf

Simple but Flexible Recommendation Engine in PyTorch

simpleitk-notebooks

Jupyter notebooks for learning how to use SimpleITK

skeeting

skeeting-page

strava-summary

surprise

A Python scikit for building and analyzing recommender systems