tsterbak
Data Scientist, Mathematician, Machine Learning Engineer, Open Source developer
Berlin, Germany
tsterbak's Stars
tiangolo/fastapi
FastAPI framework, high performance, easy to learn, fast to code, ready for production
shap/shap
A game theoretic approach to explain the output of any machine learning model.
FavioVazquez/ds-cheatsheets
List of Data Science Cheatsheets to rule the world
ludwig-ai/ludwig
Low-code framework for building custom LLMs, neural networks, and other AI models
kedro-org/kedro
Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
yzhao062/pyod
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
snorkel-team/snorkel
A system for quickly generating training data with weak supervision
online-ml/river
🌊 Online machine learning in Python
minimaxir/textgenrnn
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
IDSIA/sacred
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
reiinakano/scikit-plot
An intuitive library to add plotting functionality to scikit-learn objects.
Kaggle/docker-python
Kaggle Python docker image
scikit-learn-contrib/category_encoders
A library of sklearn compatible categorical variable encoders
SeldonIO/alibi
Algorithms for explaining machine learning models
danijar/handout
Turn Python scripts into handouts with Markdown and figures
huggingface/transfer-learning-conv-ai
🦄 State-of-the-Art Conversational AI with Transfer Learning
VertaAI/modeldb
Open Source ML Model Versioning, Metadata, and Experiment Management
plasticityai/magnitude
A fast, efficient universal vector embedding utility package.
sicara/tf-explain
Interpretability Methods for tf.keras models with Tensorflow 2.x
jmschrei/apricot
apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html
Kenza-AI/sagify
LLMs and Machine Learning done easily
msg-systems/holmes-extractor
Information extraction from English and German texts based on predicate logic
amzn/xfer
Transfer Learning library for Deep Neural Networks.
CyberZHG/keras-multi-head
A wrapper layer for stacking layers horizontally
ELS-RD/anonymisation
Anonymization of legal cases (Fr) based on Flair embeddings
koaning/skedulord
captures logs and makes cron more fun
yuvalatzmon/SACRED_HYPEROPT_Example
A minimal example for integrating a general machine learning training script with SACRED experimental framework, and HyperOpt (Distributed Asynchronous Hyperparameter Optimization).
INWTlab/dbrequests
python package built for easy use of raw SQL within python and pandas projects
TobiasPleyer/chefkoch