FlorianWilhelm

zipline-poloniex

Poloniex bundle for zipline

polars_vs_pandas

Comparing Polars to Pandas and a small introduction

bhm-at-scale

🪜 Bayesian Hierarchical Models at Scale

lda4rec

🧮 Extended Latent Dirichlet Allocation for Collaborative Filtering in Recommender Systems.

snalu.pytorch

Implementation of Signed NALU & NAC for all real numbers in PyTorch.

mlstm4reco

Multiplicative LSTM for Recommendations

boston_housing

Just a demonstration how to use JupterLab with custom packages

lrann

On the Effectiveness of Low-rank Approximations in Collaborative Filtering compared to Neural Networks

FlorianWilhelm.github.io

Blog of Florian Wilhelm

coffee_snake

Run a Python script within Java

used-cars-log-trans

Example notebook

bigquery-programmatic-sql

Use Google BigQuery with Programmatic SQL by SQLAlchemy

FlorianWilhelm

GitHub Public Profile Information

hydra-example-project

Example project showing Hydra's capabilities for configuration and CLI

mixture

polars

Fast multi-threaded DataFrame library in Rust and Python

cachetools

Extensible memoizing collections and decorators

causalml

Uplift modeling and causal inference with machine learning algorithms

pelican-themes

Themes for pelican

plumage

:art: Theme for Pelican, the static site generator.

Probabilistic-Matrix-Factorization

Python Implementation of Probabilistic Matrix Factorization(PMF) Algorithm for building a recommendation system using MovieLens ml-100k | GroupLens dataset

pyro

Deep universal probabilistic programming with Python and PyTorch

rankerEval

A fast numpy-based implementation of ranking metrics for information retrieval and recommendation.

ranklib

A library of learning to rank algorithms

scikit-learn

scikit-learn: machine learning in Python

simple-icons

SVG icons for popular brands

sparklet

spotlight

Deep recommender models using PyTorch.

statistical-inferences

Assignments of Coursera "Improving your statistical inferences" course

SURFER

Sources of Java version of SURFER (which uses JavaFX 1.3.1)