Utilities for Neural Network training

Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead

Simulation-based inference toolkit

Hyperparameter optimization that enables researchers to experiment, visualize, and scale quickly.

A python package of Zeroth-Order Optimization (ZOOpt)

📓 Long(er) text representation and classification using Doc2Vec embeddings

Inference algorithms for models based on Luce's choice axiom

Sequential model-based optimization with a `scipy.optimize` interface

A collection of 85 minority oversampling techniques (SMOTE) for imbalanced learning with multi-class oversampling and model selection features

Python module for machine learning time series:

🌲 Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams

A python chatbot framework with Natural Language Understanding and Artificial Intelligence.

Implementation of EfficientNet model. Keras and TensorFlow Keras.

Python package to analyse, validate, fill and visualise data acquired in the context of (waste) water treatment

Beautiful visualizations of how language differs among document types.

✨Fast Coreference Resolution in spaCy with Neural Networks

Convert number words (eg. twenty one) to numeric digits (21)

Facial Expression Recognition with a deep neural network as a PyPI package

Anime-themed personal assistant and goal-oriented intelligent agent

Upscale an image by a factor of 4, while generating photo-realistic details.

Predict hourly weather features given historical data for a specific location

Python3 implementation of the Schwartz-Hearst algorithm for extracting abbreviation-definition pairs

Python port of Moses tokenizer, truecaser and normalizer

Multilingual Rapid Automatic Keyword Extraction (RAKE) for Python

Python Implementations of Word Sense Disambiguation (WSD) Technologies.

Converts spoken words into text form.

Light-Weight RefineNet for Real-Time Semantic Segmentation

Improved Fisher Vector Implementation- extracts Fisher Vector features from your data

Automated Machine Learning [AutoML] with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost

Machine Learning as a Service for HEP