This Specialization is for engineers, graduate students, and ML practitioners who want to understand what happens beneath the framework layer. Across six courses, you will compare GPU and dataflow architectures, trace compilation from PyTorch to accelerator execution, analyze memory systems, debug across the stack, and build performance models through practical labs.
The goal is not to memorize one platform, but to develop the architectural intuition to work across current and emerging AI accelerators.
