mlsanityData to 14 Oct 2026

Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping

arXiv 2409.09537 ↗First posted 14 Sep 2024cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 14 Sep 2024.

Builds on

16 of 30 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  6. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  7. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  8. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  9. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  10. 2020Big Bird: Transformers for Longer Sequencescited by 1,684
  11. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  12. 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
4 more
  1. 2023Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learningcited by 7
  2. 2022An Additive Autoencoder for Dimension Estimationcited by 3
  3. 2023Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritiscited by 3
  4. 2023Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classificationcited by 1

Built on by

Not cited yet.