An open API service providing repository metadata for many open source software ecosystems.

Topic: "attention-models"

implus/PytorchInsight

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

Language: Python - Size: 5.09 MB - Last synced at: 10 months ago - Pushed at: over 4 years ago - Stars: 857 - Forks: 122

bryanlimy/clinical-super-mri

Code for "Deep Attention Super-Resolution of Brain Magnetic Resonance Images Acquired Under Clinical Protocols".

Language: Python - Size: 8.87 MB - Last synced at: 3 days ago - Pushed at: over 2 years ago - Stars: 13 - Forks: 1

Devnetly/Breast-Cancer-Classification

This repository contains some comprehensive approaches for the purpose of classifying breast cancer tissue using whole slide images (WSIs).

Language: Python - Size: 532 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 4 - Forks: 1

marinaramalhete/DeepLearningAI_NLP_Specialization

This repository is dedicated to course notebooks and personal notes from my learning during the specialization.

Language: Jupyter Notebook - Size: 352 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 2 - Forks: 0

DaraVaram/Lightweight-ViTs-for-Medical-Diagnostics

Official Keras implementation for "On-Edge Deployment of Vision Transformers for Medical Diagnostics: A Study on the Kvasir-Capsule Dataset."

Language: Python - Size: 1.07 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 0 - Forks: 0

Omar-Al-Sharif/Men-El-Akher

A transformer-based arabic text summarizer trained on 160,000 Arabic Articles to extract key information and generate concise arabic summaries

Language: Jupyter Notebook - Size: 1.74 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

mrodriguezsanz/DeepLearningAI-DeepLearningSpecialization

Participants in this Specialization have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. They learn to enhance these networks with techniques such as Dropout, BatchNorm, Xavier/He initialization, among others.

Language: Jupyter Notebook - Size: 14.3 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0