Metric learning algorithms in Python
The main features of scikit-learn-contrib/metric-learn are: Deep Learning Frameworks, Regression and Classification.
Open-source alternatives to scikit-learn-contrib/metric-learn include: devamoghs/machine-learning-with-python — This repository serves as an educational collection of practical examples and tutorials designed to facilitate the… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… alrojo/tensorflow-tutorial — Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE. amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… andersbll/deeppy — Deep learning in Python. alankbi/detecto — Build fully-functioning computer vision models with PyTorch.
This repository serves as an educational collection of practical examples and tutorials designed to facilitate the study of machine learning and data science concepts using Python. It provides a structured environment for learning core algorithms and data analysis techniques through hands-on implementation and iterative exploration. The project covers a broad range of analytical capabilities, including predictive modeling for regression, classification, and clustering tasks, as well as network topology analysis for identifying influence patterns in interconnected data. It also incorporates na
Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for deep learning models. It provides a collection of transformations that modify pixel values and spatial geometry to increase the diversity of training samples and improve model generalization. The library supports both 2D image augmentation and 3D volumetric data augmentation. It handles a variety of labels alongside images, ensuring that bounding boxes, keypoints, and segmentation masks remain accurately aligned when spatial transformations are applied. The tool incorporates
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE
