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

Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting ...

A low-dimensional voice latent space derived from deep learning captures speaker-identity representations in the temporal voice areas and supports reconstruction of voices preserving identity ...

Abstract: This paper presents attention-based deep neural networks for high-dimensional microwave modeling to predict behavior of spatio-temporal modulated (STM) non-reciprocal bandpass filters ...

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 ...

The current machine_learning directory in TheAlgorithms/Python lacks implementations of neural network optimizers, which are fundamental to training deep learning models effectively. To fill this gap ...

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 ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech researchers have been developing a neural network made out of strands of DNA instead ...

1 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China 2 Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China ...

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 ...