In graduate school, I researched robotics and the implementation of image recognition. However, once I finished school my opportunities to program lessened. But even so, my passion for programming stuck with me, and now when I have free time I do experimental projects at home using GPUEATER along with GAN video production system. When I started with deep learning the cost was a huge barrier for me. But, I happened to come across GPUEATER and that let me get my start. For me it's a service that gives you the support you need when you're about to embark on a new challenge.

Our research group at Quazar Technologies is researching how to use Electrons in two-dimensional materials in devices. These studies are highly computationally intensive, requiring the solution of several million variables for a single calculation. Such calculations have typically been restricted to academic supercomputers. However, our testing has revealed that these can also be efficiently performed on the AMD GPU nodes at GPUEater.com. We are therefore excited to use commercial cloud computing for cost-effective scientific computation. In addition, we think cloud computing can enable new and efficient computing workflows that can maximize scientific productivity.

In machine learning, the only options are to purchase an expensive GPU or to make use of a GPU instance, and GPUs made by NVIDIA hold the majority of the market share. However, a new option has been proposed by GPUEATER. GPUEATER provides NVIDIA Cloud for inference and AMD GPU clouds for machine learning. I'm sure that you will find an affordable service that meets your needs.