Developing AI applications start with training deep neural networks with large datasets. GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++. Every major deep learning framework such as TensorFlow, PyTorch and others, are already GPU-accelerated, so data scientists and researchers can get productive in minutes without any GPU programming.
Viet Anh Nguyen was awarded the Jetson Project of the Month for his Advanced Driver Assistance System (ADAS). This prototype, which runs on a NVIDIA Jetson Nano, aids a driver with collision, lane departure and speeding warnings. … Read more
This month we spotlight Rommie Amaro, professor and endowed chair in the Department of Chemistry and Biochemistry at the University of California, San Diego. … Read more
With this release, use cases such as heat sinks, data center cooling, aerodynamics and deformation of solids in linear elastic regime can be solved. … Read more
NVIDIA recently announced the Applied Research Accelerator Program. The program supports applied research on NVIDIA platforms for GPU-accelerated application deployments.
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Determined AI’s application available in the NVIDIA NGC catalog, a GPU-optimized hub for AI applications, provides an open-source platform that enables deep learning engineers to focus on building models and not managing infrastructure. … Read more
Data augmentation technique enables AI model to emulate artwork from a small dataset from the Metropolitan Museum of Art — and opens up new potential applications in fields like healthcare. … Read more
Researchers, developers, and engineers from all over the world are gathering virtually this year for the 2020 Neural Information Processing Systems (NeurlPS). NVIDIA Research will present its research through spotlight and posters. … Read more