Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...

At the core of every AI coding agent is a technology called a large language model (LLM), which is a type of neural network ...

Graph Neural Networks (GNNs) have become a powerful tool in order to learn from graph-structured data. Their ability to capture complex relationships and dependencies within graph structures, allows ...

This repository contains an efficient implementation of Kolmogorov-Arnold Network (KAN). The original implementation of KAN is available here. The problem is in the sparsification which is claimed to ...

Abstract: Deep learning is a powerful technique for data-driven learning in the era of Big Data. However, most deep learning models are deterministic models that ignore the uncertainty of data. Fuzzy ...