Deep learning is a subset of AI and machine learning that uses multilayer artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and etc.
Deep learning differs from traditional machine learning techniques in that they can automatically learn representations of data such as images, video or text without introducing hand coding rules or human domain knowledge. Their highly flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided with more data.
Responsible for many breakthroughs in AI
Such as AlphaGo from Google DeepMind
Deep learning is responsible for many of the recent breakthroughs in AI, such as the AlphaGo from Google DeepMind, self-driving cars, intelligent voice assistants and much more. With NVIDIA GPU accelerated deep learning frameworks, researchers and data scientists can significantly speed up the deep learning training, which could otherwise take days and weeks to just a few hours and days. When models are ready for deployment, developers can rely on GPU-accelerated inference platforms for the cloud, embedded device or self-driving cars to deliver powerful, low latency inference for the most computationally intensive deep neural networks.
Building a platform for deep learning goes far beyond selecting a server and GPUs. To implement AI in your company, it involves carefully selecting and integrating complex software with hardware.
Deep Learning Server
with 16x GPUs
Based on the software used, we find which hardware is most suitable for this. There are several solutions that are mostly based on GPU computing ranging from 1 GPU to for example 16 GPU servers of the brands Supermicro, Asus, Gigabyte, AIC and nVidia.