noisrucer/deep-learning-papers
DL research paper implementations with PyTorch
This project helps deep learning practitioners understand and implement complex research papers. It takes academic papers describing advanced image classification, object detection, and semantic segmentation techniques, and provides clear reviews along with working PyTorch code. Data scientists, machine learning engineers, and AI researchers can use this to quickly grasp and apply new deep learning models.
No commits in the last 6 months.
Use this if you need to quickly learn about and implement state-of-the-art deep learning models from research papers in areas like computer vision.
Not ideal if you are looking for a high-level API for readily available models, as this focuses on understanding and implementing from scratch.
Stars
64
Forks
15
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 16, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/noisrucer/deep-learning-papers"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
kk7nc/RMDL
RMDL: Random Multimodel Deep Learning for Classification
MaximeVandegar/Papers-in-100-Lines-of-Code
Implementation of papers in 100 lines of code.
OML-Team/open-metric-learning
Metric learning and retrieval pipelines, models and zoo.
miguelvr/dropblock
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
DLTK/DLTK
Deep Learning Toolkit for Medical Image Analysis