Once the training data is prepared, a distributed MPI application is then used to adjust the parameters of the machine- or deep-learning model through a ‘training’ or optimization procedure. All ...

In a paper in PLOS Computational Biology, the SFSU team published a step-by-step machine learning tutorial for beginners. Other than Biology Professor Pleuni Pennings, the remaining seven ...

We wondered what Rocklin's view on this is, and what he sees as the greatest challenges and opportunities for distributed machine learning going forward. Also: For CockroachDB, transactions ...

“In addition to applications in the military domain, coresets and distributed machine learning in general are also widely applicable in the commercial setting, where multiple organizations would ...

A new algorithm is enabling deep learning that is more collaborative and communication-efficient than traditional methods. Army researchers developed algorithms that facilitate distributed ...

Google today announced the launch of version 0.8 of TensorFlow, its open source library for doing the hard computation work that makes machine learning ... The company says distributed computing ...

By integrating LoRa technology with distributed machine learning, the network connectivity of green intelligent transportation systems can be optimized. Applying LoRa technology to the monitoring ...