Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
This provides a tool for ML researchers and practitioners to streamline prototyping, but it is incremental as it builds on existing frameworks like TensorFlow and Scikit-learn.
The paper tackles the problem of time-consuming machine learning prototyping steps by introducing the Deep Fast Machine Learning Utils (DFMLU) library, which automates and enhances tasks like model architecture prototyping, feature selection, and dataset preparation, as demonstrated through Python examples.
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.