Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...

Abstract: This paper introduces a control framework that leverages Lagrangian neural networks (LNNs) for computed torque control (CTC) of robotic systems with unknown dynamics. Unlike prior LNN-based ...

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

3D rendering—the process of converting three-dimensional models into two-dimensional images—is a foundational technology in computer graphics, widely used across gaming, film, virtual reality, and ...

While the project includes an initial XOR experiment to build intuition, this documentation focuses solely on the more complex MNIST experiment. Much of the mathematical insight was drawn from the ...

This is my journey to implement NNs from first principles, one neuron at a time. In this notebook we build a neural network with 2 neurons in layer 1, and 1 neuron in layer 2. We then visualize how it ...