Dheeraj Vaddepally explores the post-cloud era of mobile development, highlighting how Edge AI improves app speed, data privacy, and reliability on devices.

Akida FPGA Cloud service provides a pre-configured environment where designers can upload their models—created using standard ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

As technology progresses, so does the demand for artificial intelligence (AI) and machine learning (ML). As such, the tools that enable developers to build powerful models have to keep up with the ...

TensorFlow is one of the best software libraries suited for AI and machine learning. It was developed by the Google Brain Team by partnering with Google’s machine intelligence research organization.

This is new: TensorFlow 2.18 integrates the current version 2.0 of NumPy and, with Hermetic CUDA, will no longer require local CUDA libraries during the build. The ...

At this period, TensorFlow and PyTorch are two of the most famous frameworks within deep learning. Each has its merits and demerits and serves different purposes for the AI and machine learning ...

Google is rebranding TensorFlow Lite to LiteRT (as in “lite runtime”). This lets you deploy ML and AI models on Android, iOS, and embedded devices. Basically, for on-device AI at the Edge. Google ...

ImportError: DLL load failed while importing _pywrap_tensorflow_internal: A dynamic link library (DLL) initialization routine failed. Failed to load the native ...