Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment The importance of machine learning and deep learning is no longer in ...

Religious wars have been a cornerstone in tech. Whether it’s debating about the pros and cons of different operating systems, cloud providers, or deep learning frameworks — a few beers in, the facts ...

Overview: MLOps keeps machine learning models stable, updated, and easy to manage.Python tools make every step of machine learning simpler and more reliable.MLO ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Google today announced the launch of TensorFlow Quantum, bringing ...

At version r1.5, Google's open source machine learning and neural network library is more capable, more mature, and easier to learn and use If you looked at TensorFlow as a deep learning framework ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The open-source TensorFlow machine learning library is getting faster, ...

It's possible to create neural networks from raw code. But there are many code libraries you can use to speed up the process. These libraries include Microsoft CNTK, Google TensorFlow, Theano, PyTorch ...

Besides putting a Raspberry Pi to work on a mini Mars rover, it's now going to be a lot easier to use Google's TensorFlow artificial-intelligence framework with the low-powered computer. Developers ...

TensorFlow has become the most popular tool and framework for machine learning in a short span of time. It enjoys tremendous popularity among ML engineers and developers. According to the Hacker News ...