TensorFlow gives public access to the tools used by Google’s machine learning team.

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 ...

Machine learning isn’t something you buy but something you do. Use TensorFlow to experiment now with machine learning so you can build it into your DNA Machine learning is still a pipe dream for ...

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

TensorFlow Quantum (TFQ) brings Google quantum computing framework Cirq and TensorFlow together to enable the creation of quantum machine learning (ML) models.

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 ...

TensorFlow allows data scientists to use Python to work with high-level abstractions of the data sets and complex mathematical expressions that make up a machine learning application.

Caffe, CNTK, DeepLearning4j, Keras, MXNet, and TensorFlow are deep learning frameworks. Scikit-learn and Spark MLlib are machine learning frameworks. Theano straddles both categories.

Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.

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