YohannLeFaou's Stars
meta-llama/llama
Inference code for Llama models
huggingface/pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
freqtrade/freqtrade
Free, open source crypto trading bot
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
DataTalksClub/data-engineering-zoomcamp
Free Data Engineering course!
ivy-llc/ivy
Convert Machine Learning Code Between Frameworks
guipsamora/pandas_exercises
Practice your pandas skills!
blue-yonder/tsfresh
Automatic extraction of relevant features from time series:
Chainlit/chainlit
Build Conversational AI in minutes ⚡️
cleverhans-lab/cleverhans
An adversarial example library for constructing attacks, building defenses, and benchmarking both
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
Giskard-AI/giskard
🐢 Open-Source Evaluation & Testing for ML models & LLMs
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
mito-ds/mito
The mitosheet package, trymito.io, and other public Mito code.
scikit-learn-contrib/MAPIE
A scikit-learn-compatible module to estimate prediction intervals and control risks based on conformal predictions.
skrub-data/skrub
Prepping tables for machine learning
cloud-carbon-footprint/cloud-carbon-footprint
Cloud Carbon Footprint is a tool to estimate energy use (kilowatt-hours) and carbon emissions (metric tons CO2e) from public cloud usage
caesar0301/treelib
An efficient implementation of tree data structure in python 2/3.
etetoolkit/ete
Python package for building, comparing, annotating, manipulating and visualising trees. It provides a comprehensive API and a collection of command line tools, including utilities to work with the NCBI taxonomy tree.
cerlymarco/shap-hypetune
A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.
sylver-dev/sylver-cli
zelros/cinnamon
CinnaMon is a Python library which offers a number of tools to detect, explain, and correct data drift in a machine learning system
squirrel-prover/squirrel-prover
The Squirrel Prover repository. An interactive prover for the formal verification of security protocols.
Capgemini-Invent-France/CarbonAI
Python package to monitor the power consumption of any algorithm
msangnier/optboosting
Accelerated gradient and proximal boosting in Python
ProjectBabbage/mlim
ProjectBabbage/babbagecoin
Project Babbage 2nd edition: a PoW blockchain
ProjectBabbage/capt
[ Proof of concept ]: manage and describe a project to any level of details
ProjectBabbage/render3d
Project Babbage 1st edition: a 3D renderer built from scratch for the purpose of learning.
RBeaudet/ipid
IPID document parsing