Showing 1-14 of 14 projects
A comprehensive repository covering papers, codes, datasets, tutorials, and applications for transfer learning, domain adaptation, and more.
Automated Machine Learning with scikit-learn
A PyTorch library for meta-learning research, enabling few-shot, fine-tuning, and other advanced ML techniques.
A Python library for meta-learning that enables fast adaptation of deep neural networks.
Implementation of papers in 100 lines of code
Elegant PyTorch implementation of the Model-Agnostic Meta-Learning (MAML) paper for AI/ML developers.
A collection of resources for few-shot learning (FSL) in Python, useful for vibe coders building AI-powered applications.
A curated list of resources for meta-learning, including papers, code, books, and more for developers working with AI tools.
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
A repository for few-shot learning machine learning projects, focused on meta-learning and PyTorch.
A collection of notebooks for learning various meta-learning techniques like MAML, Reptile, and One-Shot Learning.
PyTorch code for a CVPR 2018 paper on few-shot learning, a technique for training ML models with limited data.
A comprehensive library for few-shot learning in Python with PyTorch, featuring state-of-the-art algorithms.
A research paper on first-order meta-learning algorithms, with associated code in JavaScript.
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