In the realm of machine learning frameworks, there’s no one-size-fits-all solution. PyTorch and TensorFlow offer distinct advantages that cater to different aspects of the machine learning workflow.

Conclusion Exploring machine learning with TensorFlow on Ubuntu opens a world of possibilities. Whether you're a beginner or an experienced practitioner, the combination of TensorFlow's powerful ...

At QCon SF, Daniel Situnayake presented "Machine learning on mobile and edge devices with TensorFlow Lite". TensorFlow Lite is a production-ready, cross-platform framework for deploying ML on ...

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices ...

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 possible. Normally, a small point update ...

With this week's release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.

TensorFlow seems to perform as well as anything out there for neural network and deep learning training, despite an early benchmark that falsely indicated otherwise because of differing GPU libraries.

Since announcements late last year about Google open-sourcing TensorFlow, the company’s open-source library for machine learning, and previous coverage at InfoQ, the data-science community has ...

Other optimizations to TensorFlow components resulted in significant CPU performance gains for various deep learning models. Using the Intel MKL imalloc routine, both TensorFlow and the Intel MKL-DNN ...

TensorFlow 0.8 adds distributed computing support to speed up the learning process for Google's machine learning system.