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

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

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

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

Pseudo-3D CNN networks in PyTorch.

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.