End-Users (individuals and companies) require huge GPU computation power for machine learning algorithms. They pay with MRDS tokens for learning network power.
Large models in machine learning can dramatically improve overall performance. With the advent of deep learning, the field is rapidly expanding. However, large neural network models face infrastructure limitations. These limitations can be overcome in several ways. One approach is to develop more powerful graphical processing units (GPUs) that can handle the computational load. Another approach is to distribute the load to clients speaking to a central server for batch training updates. Thus it is possible to crowdsource the computational power necessary to train models and incentivize participation via a Myriads.IO token-based network