The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

Automated methods enable the analysis of PET/CT scans (left) to accurately predict tumor location and size (right). Credit: Nature Machine Intelligence (2024). DOI: 10.1038/s42256-024-00912-9 ...

A Deep Learning Alternative Can Help AI Agents Gameplay the Real World A new machine learning approach tries to better emulate the human brain, in hopes of creating more capable agentic AI.

Ali Momeni, Babak Rahmani, Matthieu Malléjac, Philipp del Hougne, Romain Fleury. Backpropagation-free training of deep physical neural networks. Science, 2023; DOI: 10.1126/science.adi8474 ...

Furthermore, deep learning algorithms trained with optical coherence tomography (OCT) data can detect microstructural damage due to glaucoma and its progression over time.

From machine learning and deep learning to generative AI and natural language processing, different types of AI models serve various use cases—for example, automating tasks, developing better ...

Deep learning is a branch of AI—specifically, a subset of machine learning (ML) —that involves the use of artificial neural networks to autonomously learn complex patterns and make intelligent ...

Deep learning algorithm used to pinpoint potential disease-causing variants in non-coding regions of the human genome The methods help identify 'footprints' that indicate binding sites and reveal ...

Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) for postoperative mortality of 9.17 (95% CI, 5.85-13.82) compared with an unadjusted OR of 2.08 (0. ...

Deep Learning (DL) is, in essence, Machine Learning on steroids. It’s a specialized subfield that focuses on algorithms inspired by the structure of the brain, known as artificial neural networks.