We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Deep learning uses multi-layered neural networks that learn from data through predictions, error correction and parameter ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

The researchers combined temporal convolutional networks and long short-term memory networks for analysing the signals.

Recent advances in artificial intelligence (AI) and machine learning (ML) have transformed our ability to decode complex ...

A deep learning framework enhances medical image recognition by optimizing RNN architectures with LSTM, GRU, multimodal fusion, and CNN integration. It improves dynamic lesion detection, temporal ...

Patients at primary care clinics received more dementia diagnoses after implementation of a machine-learning tool designed to ...

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting? Based on our evaluation of all models from both classes ever used in ...

A paper co-authored by Prof. Alex Lew has been selected as one of four "Outstanding Papers" at this year's Conference on Language Modeling (COLM 2025), held in Montreal in October.

Despite advanced algorithms and automation, one truth remains: Effective cybersecurity requires a careful balance between ...