TensorFlow

TensorFlow is an open-source software library for numerical computation and large-scale machine learning. Developed by the Google Brain team, it provides a flexible platform for building and deploying machine learning models across a range of environments, from research workstations to data centers and mobile devices. Core to TensorFlow is the dataflow graph concept, in which nodes represent mathematical operations and edges represent tensors that flow between them. The framework supports automatic differentiation and scalable execution on CPUs, GPUs, and Google's Tensor Processing Units (TPUs).

TensorFlow began as an internal Google project and was released as an open-source project in 2015. It has since evolved through multiple versions, with TensorFlow 2.x (released in 2019) emphasizing ease of use, eager execution by default, tighter integration with the Keras high-level API, and a more Pythonic interface.

Key components include TensorFlow Core, the low-level API; Keras for rapid model prototyping; TensorBoard for visualization; TensorFlow Serving for production model deployment; TensorFlow Lite for mobile and embedded devices; and TensorFlow.js for in-browser machine learning. The library also includes tools for data input pipelines (tf.data) and distributed training strategies to scale across multiple devices.

TensorFlow is widely used in research and industry for tasks such as image and speech recognition, natural language processing, and reinforcement learning. The ecosystem supports multiple languages, with Python as the primary API, and bindings or wrappers for C++, Java, and JavaScript, among others. It is released under the Apache 2.0 license and enjoys a large community of contributors and extensive documentation.