This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

Deep Learning Crash Course: A Hands-On, Project-Based Introduction to Artificial Intelligence is written by Giovanni Volpe, Benjamin Midtvedt, Jesús Pineda, Henrik Klein Moberg, Harshith Bachimanchi, ...

We dive into Transformers in Deep Learning, a revolutionary architecture that powers today's cutting-edge models like GPT and BERT. We’ll break down the core concepts behind attention mechanisms, self ...

Density Functional Theory (DFT) is the most widely used electronic structure method for predicting the properties of molecules and materials. Although DFT is, in principle, an exact reformulation of ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Abstract: This study explores the application and practice of deep learning algorithms and MATLAB software in teaching higher mathematics. The teaching case design of function approximation, ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

Artificial Intelligence systems powered by deep learning are changing how we work, communicate, and make decisions. If we want these technologies to serve society responsibly, tomorrow’s citizens need ...

This project presents a complete workflow for cone detection in Formula Student Driverless scenarios using deep learning. It demonstrates how to use MATLAB® and Simulink® for data preparation and ...