Abstract: The emergence of Deep Learning compilers provides automated optimization and compilation across Deep Learning frameworks and hardware platforms, which enhances the performance of AI service ...

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 ...

This group project explores the use of neural networks to model avalanche hazard forecasts using a 15-year dataset from the Scottish Avalanche Information Service (SAIS). Our group has been assigned ...

Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...

🎉 Welcome to your very first assignment in Machine Programming! In this journey, you’ll get your hands dirty with inductive program synthesis, starting with a bottom-up synthesizer, and ending with a ...

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 ...

Abstract: Recent advances in hardware and software technology have made it possible to implement more resourcedemanding deep learning algorithms in constrained hardware environments. This creates ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...

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 ...

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from ...