Recent advances in neural network methodologies have revolutionised the analysis and classification of chromosome images, streamlining traditionally labour‐intensive processes in cytogenetics ...

Hyperspectral image classification has emerged as a transformative technology in Earth observation, providing unprecedented detail through hundreds of contiguous spectral bands. Neural network ...

Dr. James McCaffrey of Microsoft Research details the 'Hello World' of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset.

Next, the demo creates and trains a neural network model using the MLPClassifier module ("multi-layer perceptron," an old term for a neural network) from the scikit library. [Click on image for larger ...

In other words, despite the staggering complexity of neural networks, classifying images -- one of the foundational tasks for AI systems -- requires only a small fraction of that complexity.

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI.

Over the past decades, computer scientists have developed many computational tools that can analyze and interpret images.

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds ...