Feature engineering: A deep learning algorithm can save time because it doesn’t require humans to extract features manually from raw data.

Self-driving cars know their own way in unpredictable traffic, thanks to path planning technology. Among current AI-driven ...

One in every three people is expected to have cancer in their lifetime, making it a major health concern for mankind. A crucial indicator of the outcome of cancer is its tumor microsatellite ...

Precision medicine is a fast-growing field whereby medical treatments are tailored to individual patients – taking factors like genetics and lifestyle into account. A key part of this process is ...

The accumulation of misfolded proteins in the brain is central to the progression of neurodegenerative diseases like ...

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. ...

It’s important to note that, in the domain of AI (i.e., computers that can imitate human intellect and behavior), deep learning is a subset of ML, and ML is a subset of AI. Commonly, ML algorithms ...

For the algorithm training, the participating teams had access to a large annotated PET/CT dataset. All algorithms submitted for the final phase of the competition are based on deep learning methods.

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

As deep learning algorithms become more sophisticated, their applications have expanded widely, from security to education to transportation. Here are some of the most common applications: ...

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.