Scientists have built a "thermodynamic computer" that can produce images from random disturbances in data, that is, noise. In ...

When Covid-19 struck in 2020, Sashikumaar Ganeshan at the Indian Institute of Science, Bangalore built a model to predict the spread of the contagion, marking his deep immersion into AI technologies.

A threefold hippocampal code across conceptual directions, phase-locked to entorhinal grid activity, reveals a periodic mechanism through which entorhinal grids structure hippocampal vector ...

Eric Gutiérrez, 6th February 2026. A Python implementation of a 1-hidden layer neural network built entirely from first principles. This project avoids deep learning libraries (like TensorFlow or ...

In this work we present two main contributions: the first one is a Python implementation of the discrete approximation of the Laplace-Beltrami operator (LBO) (Belkin et al., 2008) allowing us to solve ...

The package contains a mixture of classic decoding methods and modern machine learning methods. For regression, we currently include: Wiener Filter, Wiener Cascade, Kalman Filter, Naive Bayes, Support ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

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 ...

Abstract: In this article, in order to apply neural networks to the educational process, the part of algorithms and programming of programming subject structured the content of teaching materials on ...

The series is designed as an accessible introduction for individuals with minimal programming background who wish to develop practical skills in implementing neural networks from first principles and ...