Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Huge volumes of data need near-supercomputer power to process and analyze it all. You can get there with the .NET Task Parallel Library. As Web and mobile applications face the challenge of quickly ...

What if you could train massive machine learning models in half the time without compromising performance? For researchers and developers tackling the ever-growing complexity of AI, this isn’t just a ...

Data science is hard work, not a magical incantation. Whether an AI model performs as advertised depends on how well it’s been trained, and there’s no “one size fits all” approach for training AI ...

As hardware designers turn toward multicore processors to improve computing power, software programmers must find new programming strategies that harness the power of parallel computing. One technique ...