LCGC International’s interview series on the evolving role of artificial intelligence (AI)/machine learning (ML) in separation science continues with Boudewijn Hollebrands from Unilever Foods R&D, ...

Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Artificial intelligence now plays Go, paints pictures, and even converses like a human. However, there remains a decisive difference: AI requires far more electricity than the human brain to operate.

In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept ...

Type 1 diabetes (T1D) is an autoimmune condition in which the body's own immune system attacks insulin-producing cells. As a result, patients with T1D must closely monitor their blood glucose (BG) ...

Domain adaptation may be a novel creative solution to predict infection risk in patients with chronic lymphocytic leukemia ...

An AI model with the potential to transform cervical spondylosis diagnosis by spotting subtle vertebral changes quickly and accurately.

University of Warwick research warns that popular deep learning systems trained for cancer pathology may be relying on hidden ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Integrating deep learning with traditional forecasting techniques can improve early warning systems by capitalizing on each approach’s respective advantages.

Overview PyTorch courses focus strongly on real-world Deep Learning projects and production skills.Transformer models and NLP training are now core parts of mos ...