Overview: Master deep learning with these 10 essential books blending math, code, and real-world AI applications for lasting expertise.From neural networks to N ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...

Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...

A research team from the Aerospace Information Research Institute of the Chinese Academy of Sciences (AIRCAS) has developed a ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Bridging speed and accuracy in radiation therapy QA Led by Professor Fu Jin, the study addresses a critical challenge in ...

For example, a Convolutional Neural Network (CNN) trained on thousands of radar echoes can recognize the unique spatial signature of a small metallic fragment, even when its signal is partially masked ...

Detecting concealed explosives and chemical threats constitutes a critical challenge in global security, yet current ...

Model predicts effect of mutations on sequences up to 1 million base pairs in length and is adept at tackling complex ...

But this breakthrough doesn't stand alone; recent developments in plastic science and recycling include pyrolysis research to ...

In a study titled Recent Applications of Machine Learning Algorithms for Pesticide Analysis in Food Samples, published in the ...