Abstract: Smart cameras are a widespread technology for image recognition and robotic vision. However, these devices face bandwidth limitations related with the high number of pixels transmitted and ...

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

What happens when intelligence moves off the cloud and onto the device? Edge AI Studio cuts latency, improves performance, ...

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

A production-ready deep learning project for time-series image classification using EfficientNet/NFNet with PyTorch Lightning. This project implements transfer learning for multi-class classification ...

This project implements ResNet-50, a deep convolutional neural network with 50 layers that uses residual connections to enable training of very deep networks. The architecture includes identity ...

Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts!

Abstract: Deep learning-based approaches to hyperspectral image analysis have attracted large attention and exhibited high performance in image classification tasks. However, deployment of deep ...

1 School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China 2 Department of Mechanical and Electrical Engineering, Henan Vocational College of ...