Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)
This repo contains various data science strategy and machine learning models to deal with structure as well as unstructured data. It contains module on feature-preprocessing, feature-engineering, machine-learning-models, bayesian-parameter-tuning, etc, built using libraries such as scikit-learn, keras, h2o, xgboost, lightgbm, catboost, etc.
Semantics Driven Embedding Learning for Effective Entity Alignment, ICDE 2022
A small library that can encode categorical variables to entity embeddings using a TensorFlow 2.0 neural network. Supports classification and regression problems. Network parameters are adjustable.
Train word2vec and keeps all entities regardless of their frequency
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