一个加速机器学习原型开发的Python工具库，简化模型与数据流程。
Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
- 集成神经网络搜索、高级特征选择与数据管理工具
- 支持TensorFlow/Keras/Scikit-learn，提升开发效率
- 适合快速实验与工程化落地的开发者
机器学习研究与应用常涉及模型架构原型设计、特征选择和数据准备等耗时环节。为此，我们提出Deep Fast Machine Learning Utils（DFMLU）库，提供自动化与增强相关流程的工具。该库兼容TensorFlow、Keras与Scikit-learn，涵盖密集神经网络搜索方法、高级特征选择技术及数据管理与训练结果可视化工具。本文介绍其功能并提供各模块的Python使用示例，助力高效原型开发。
原文摘要 · Abstract (English)
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.
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