Yet Another Library for Deep Learning You Should Know About

It has many algorithms, supports sparse datasets, is fast and has many utility functions, like cross-validation, grid search, etc. When it comes to advanced modeling, scikit-learn many times falls shorts. If you need Boosting, Neural Networks or t-SNE, it's better to avoid scikit-learn. While MLPClassifier and MLPRegressor have a rich set of arguments, there's no option to customize layers of a Neural Network (beyond setting the number of hidden units for each layer) and there's no GPU support. While there are already superior libraries available like PyTorch or Tensorflow, scikit-neuralnetwork may be a good choice for those coming from a scikit-learn ecosystem.