Deep learning has advanced rapidly, driving breakthroughs in image recognition, natural language processing, and autonomous ...

There is no real middle ground when it comes to TensorFlow use cases. Most implementations take place either in a single node or at the drastic Google-scale, with few scalability stories in between.

Ideally, you’d use a computer with a GPU but that’s optional, the difference being between three or twenty-four hours of training.

Intel adds its new Data Center GPU Flex Series to Pluggable Devices, something Intel is calling Intel Extension for TensorFlow, available right now.

If you are looking to get started with TensorFlow yourself, we’ve covered quite a few tutorials. On the other hand, we talk quite a bit about Fourier transforms, too.

Every major deep-learning framework, such as TensorFlow, PyTorch, and others, is already GPU-accelerated, so data scientists and researchers can get up to speed without GPU programming.