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anomalydetector

SR-CNN

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

bert

TensorFlow code and pre-trained models for BERT

bert-kpe

causalml

Uplift modeling and causal inference with machine learning algorithms

darts

A python library for easy manipulation and forecasting of time series.

deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

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.

eng-edu

forecasting

Time Series Forecasting Best Practices & Examples

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

kaggle-solutions

🏅 Collection of Kaggle Solutions and Ideas 🏅

keda

KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes

luminol

Anomaly Detection and Correlation library

neural_prophet

NeuralProphet - A simple forecasting model based on Neural Networks in PyTorch

nlp-recipes

Natural Language Processing Best Practices & Examples

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

optuna

A hyperparameter optimization framework

orbit

Bayesian forecasting with object-oriented design and probabilistic models under the hood.

prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

pyflux

Open source time series library for Python

pyod

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

recommenders

Best Practices on Recommendation Systems

sktime

A unified framework for machine learning with time series

tf-quant-finance

High-performance TensorFlow library for quantitative finance.

top2vec

Top2Vec learns jointly embedded topic, document and word vectors.

transformers-interpret

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

tsfresh

Automatic extraction of relevant features from time series: