A list of the top books to learn deep learning divided into four distinct categories. Personal reviews are included for each one of them.
Implement a UNETR to perform 3D medical image segmentation on the BRATS dataset
Discorver how to formulate and train Spiking Neural Networks (SNNs) using the LIF model, and how to encode data so that it can be processed by SNNs
Learn all there is to know about transformer architectures in computer vision, aka ViT.
A mathematical explanation of the Swapping Assignments Between Views (SWAV) paper.
Explore the most popular gnn architectures such as gcn, gat, mpnn, graphsage and temporal graph networks
A self-complete guide for understanding biology concepts that are necessary for applying deep learning in biology and bioinformatics focused on protein folding and alphafold2 related stuff
Explore the most popular deep learning architecture to perform automatic speech recognition (ASR). From recurrent neural networks to convolutional and transformers.
A general perspective on understanding self-supervised representation learning methods.