Deep neural networks (DNNs) have become a cornerstone of modern AI technology, driving a thriving field of research in ...

Bridging communication gaps between hearing and hearing-impaired individuals is an important challenge in assistive technology and inclusive education. In an attempt to close that gap, I developed a ...

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 ...

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 ...

We propose the Generalized Box–Cox Transformation (GBCT), a nonlinear scaling method that alleviates multiscale stiffness by compressing multi-magnitude data. Integrated into our data-driven framework ...

In the industry context, these deep neural networks are driving innovation across sectors such as healthcare, where layered models analyze medical imaging for diagnostics, and finance, where they ...

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 ...

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: This work investigates the generalization behavior of deep neural networks (DNNs), focusing on the phenomenon of “fooling examples,” where DNNs confidently classify inputs that appear random ...

Abstract: Unlike traditional feedforward neural networks, recurrent neural networks (RNNs) possess a recurrent connection that allows them to retain past information. This internal memory enables RNNs ...