Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
Fabi Prezja
TL;DR
An overview of DFMLU's functionalities is presented, providing Python examples for each tool, including methods for dense neural network search, advanced feature selection, and utilities for data management and visualization of training outcomes.
Abstract
Machine learning (ML) research and application often involve time-consuming steps such as model architecture prototyping, feature selection, and dataset preparation. To support these tasks, we introduce the Deep Fast Machine Learning Utils (DFMLU) library, which provides tools designed to automate and enhance aspects of these processes. Compatible with frameworks like TensorFlow, Keras, and Scikit-learn, DFMLU offers functionalities that support model development and data handling. The library includes methods for dense neural network search, advanced feature selection, and utilities for data management and visualization of training outcomes. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.