Deep Learning Solutions

Conventional ADAS technology can detect some objects, do basic classification, alert the driver of hazardous road conditions, and in some cases, slow or stop the vehicle. This level of ADAS is great for applications like blind spot monitoring, lane change assistance, and forward collision warnings.

With NVIDIA self-driving car solutions, a vehicle's ADAS can discern a police car from a taxi; an ambulance from a delivery truck; or a parked car from one that is about to pull out into traffic. It can even extend this capability to identify everything from cyclists on the pavement to absent-minded pedestrians.

Image recognition refers to a DNN's ability to identify images and objects within that image, including faces. This technology can be used for shape identification for modeling purposes, social media photo tagging, optical character recognition, and more.

Voice recognition is a rapidly developing branch of deep learning as there are numerous applications for this technology. Language translation word by word in itself isn't complicated, but often the meaning of a word or phrase is lost with simple word to word translation. Fortunately, Deep learning is ideal for processing big, complicated data sets, allowing for a more natural translation that can detect the nuances of speech and the meaning and context of a word or phrase. An example of this would be Google's Neural Machine Translation System.

Deep learning lets you uncover patterns in large data sets to reveal new knowledge and insights in hours or minutes. This results in powerful applications for deep learning such as risk and threat analysis, business analytics, and medical diagnosis

Life Sciences applications, such as Bioinformatics, Molecular Dynamics, and Quantum Chemistry are helped greatly by the massive parallel architecture of NVIDIA's GPUs.

Tesla® GPUs provide bio-physicists and computational chemists with the tools to push the boundaries of bio-chemical research, optimizing the scientific workflow and accelerating the pace of research.