The deep learning framework PyTorch has infiltrated the enterprise thanks to its relative ease of use. Three companies tell us why they chose PyTorch over Google’s renowned TensorFlow framework.

Before releasing TensorFlow, Google used to train these neural nets using a vast array of machines equipped with Graphic Processing Unit chips (its proprietary hardware) with its proprietary data.

Ztachip top level architecture The magic happens in the Ztachip core, which is mostly an array of Pcores. Each Pcore has both vector and scalar processing capability, making it super flexible.

Which of these deep learning frameworks should you use? In this article, we’ll take a high-level comparative look at TensorFlow, PyTorch, and JAX.

The easiest way to understand TensorFlow and Google's approach to AI is with image recognition. In 2011, Google created DistBelief, which used machine learning to identify what's in a photo by ...

Fortunately, with ample spare time, those who share my problem can now use an image captioning model in TensorFlow to caption their photos and put an end to the pesky first-world problem.

To help with that, [Edje Electronics] has put together a step-by-step guide to using TensorFlow to retrain Google’s Inception object recognizer.

Description A tensor processing unit (TPU)—sometimes referred to as a TensorFlow processing unit—is a special-purpose accelerator for machine learning. It is processing IC designed by Google to ...