Abstract: This paper proposes a cascaded deep convolutional neural network (CNN) architecture consisting of a data pre-processing sub-network and a task-specific sub-network, forming a hierarchically ...

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 ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

ABSTRACT: Predicting the exact duration of sick leave in patients with tuberculosis remains challenging due to the heterogeneity of recovery trajectories. This study uses machine learning to estimate ...

we implment a our own Neural Network Mini Library. In Part 2 of the project, we used PyTorch to create a Neural Network that predicts California House Prices. The python files are called ...

Computational systems have to learn when and how they should exert control over their actions. How do agents learn to solve this “metacontrol” problem? Here, we created a task that externalizes this ...

The significant contributions of this work are threefold. First, it leverages deep learning to extend in vivo imaging depth of two-photon excitation fluorescence microscopy, far beyond the depths ...