Explore 20 powerful activation functions for deep neural networks using Python! From ReLU and ELU to Sigmoid and Cosine, ...

Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node ...

Graph neural network architectures are tested for their ability to generalize using multiple data set splits, including out-of-distribution HFEs and unseen molecular scaffolds. Our most important ...

In GIGNet, multi-level graph neural networks (GNNs) are utilized to extract internal graph-based features from signal samples and correlation information between different signals treated as nodes in ...

Lecture 2: crash course on programming neural networks In the second session, we move beyond Python basics to implement a simple neural network entirely from scratch using Python and a few standard ...