TensorFlow is Google’s software library aimed at helping developers build machine-learning models. This short glossary will help you get up to speed on its terms and concepts.

From the glossary:

As machine learning becomes more common for tasks ranging from speech to facial recognition, the tools to build ML models have become more sophisticated.

TensorFlow is one of the major software libraries designed to simplify the creation of machine-learning models for developers.

It’s an open source, accelerated-math library designed to help developers build and train machine-learning models using a wide range of hardware—CPUs, GPUs, and even specialized chips such as TPUs (Tensor processing units).

While TensorFlow was originally designed for use with more powerful machines, it has evolved to be able to create models to run in all sorts of unlikely places, from browsers to low-power IoT devices. Today, TensorFlow can be used with a wide range of programming languages, including Python, Go, C++, Java, Swift, R, Julia, C#, Haskell, Rust, and JavaScript.

However, while TensorFlow streamlines the creation machine-learning models, learning the basics can still take time. Here are definitions of common terms to help you get to grips with TensorFlow.

Cloud TPU
The name for Google’s custom processor, designed to be efficient at carrying out key tasks when training or running a machine-learning model—such as executing matrix multiplications.

Each v2 TPU is capable of carrying out 180 trillion operations per second (teraflops) and sports 64GB of high-bandwidth memory for shuttling data around. The more recent v3 TPUs up the speed further and are available in pods capable of up to 100 quadrillion—one thousand trillion—operations per second, or 100 petaflops.