semisup-learn
Semi-supervised trainer
A framework for training semi-supervised machine learning models using various techniques
Semi-supervised learning frameworks for python, which allow fitting scikit-learn classifiers to partially labeled data
502 stars
25 watching
154 forks
Language: Python
last commit: almost 4 years ago
Linked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | A PyTorch implementation of a semi-supervised learning framework for training deep neural networks with noisy labels by dynamically dividing the data into clean and noisy sets. | 546 |
| | A collection of semi-supervised learning and generative models implemented in PyTorch | 707 |
| | A scikit-learn wrapper for interpretable classifiers based on decision rules | 489 |
| | A Python implementation of a distributed machine learning framework for training neural networks on multiple GPUs | 6 |
| | An implementation of semi-supervised federated learning for improving the performance of a server using distributed clients with unlabeled data | 36 |
| | Provides training materials and tools for building machine learning applications | 72 |
| | Automates large-scale deep learning training on distributed clusters, providing fault tolerance and fast recovery from failures. | 1,302 |
| | A Python library providing efficient implementations of various supervised and weakly-supervised metric learning algorithms. | 1,402 |
| | Provides a flexible and configurable framework for training deep learning models with PyTorch. | 1,196 |
| | An implementation of meta-learning from unlabeled data to improve task accuracy using a technique called 'weak supervision' | 4 |
| | A comprehensive Python module for machine learning built on top of SciPy | 60,451 |
| | Tutorials and materials for learning machine learning with Python using popular libraries like scikit-learn. | 576 |
| | Replication of Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in PyTorch for reinforcement learning tasks | 830 |
| | A tutorial on applying machine learning to practical situations using the scikit-learn library | 130 |
| | Enables larger language models to generate multi-turn multimodal instruction-response conversations from image-caption pairs with minimal annotations. | 47 |