Deep learning approaches, particularly convolutional neural networks (CNNs) and other architectures, were used in 49 papers. These models excel at image-based tasks such as land cover classification, ...

In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

WorldQuant University (WQU) has launched the Deep Learning Fundamentals Lab, a free, 16-week online certificate program ...

CONTAIN™ represents the next breakthrough in AI-powered ore sorting from TOMRA Mining – a deep learning solution purpose-built to classify complex inclusion-type ores with unprecedented accuracy. By ...

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...

Abstract: Recent advances in deep learning have significantly improved hyperspectral image (HSI) classification. However, deep learning models for HSI classification typically rely on one-hot labels, ...

This repository contains the Python code for the Master's Thesis: "Uncertainty Quantification for Deep Learning in Sleep Apnea Detection: A Comparative Evaluation of Monte Carlo Dropout and Deep ...

Deep learning has been widely applied to high-dimensional hyperspectral image classification and has achieved significant improvements in classification accuracy. However, most current hyperspectral ...

The segmentation and classification of breast ultrasound (BUS) images are crucial for the early diagnosis of breast cancer and remain a key focus in BUS image processing. Numerous machine learning and ...