A team of researchers has found a way to steer the output of large language models by manipulating specific concepts inside these models. The new method could lead to more reliable, more efficient, ...

This project explores the application of recursive neural networks (RNNs) in natural language processing, specifically for part-of-speech (POS) tagging. Drawing on foundational work by Socher et al.

Researchers from Samsung Electronic Co. Ltd. have created a tiny artificial intelligence model that punches far above its weight on certain kinds of “reasoning” tasks, challenging the industry’s ...

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 ...

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 ...

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...

Installing Python and related applications on a system without a network connection isn’t easy, but you can do it. Here’s how. The vast majority of modern software development revolves around one big ...

Abstract: To drive the gyroscope into a command vibration mode, and to simultaneously estimate the angular velocity, this paper proposes an improved fully adjusted neural network-based adaptive ...

ABSTRACT: This paper discusses the general decay synchronization problem for a class of fuzzy competitive neural networks with time-varying delays and discontinuous activation functions. Firstly, ...