TensorFlow
By Google
TensorFlow is Google's open-source machine learning framework for building, training, and deploying neural networks and other ML models at scale, from research prototypes to production systems.
Definition
TensorFlow is Google's open-source machine learning framework for building, training, and deploying neural networks and other ML models at scale, from research prototypes to production systems.
Overview
First released by Google Brain in 2015, TensorFlow provides a computational framework for defining models as graphs of mathematical operations that can run efficiently across CPUs, GPUs, and Google's own TPU accelerators. Tensors — multi-dimensional arrays — flow through these operation graphs, which gives the framework its name and lets the same model definition be optimized and distributed across many devices without the developer managing low-level parallelism by hand. Early TensorFlow required building static computation graphs before running them, which was powerful but often unintuitive. TensorFlow 2.x shifted to eager execution by default and adopted Keras as its official high-level API, making model-building feel closer to writing ordinary Python code while still compiling down to optimized graphs for training and deployment via `tf.function`. Beyond model training, the TensorFlow ecosystem includes TensorFlow Lite for mobile and embedded inference, TensorFlow.js for running models in the browser, and TensorFlow Serving for production model deployment. It remains one of the two dominant deep learning frameworks alongside PyTorch, and models built in either framework are frequently shared and fine-tuned through hubs like Hugging Face. Learners going deeper into this ecosystem often pair a course like TensorFlow & Keras with foundational material from Machine Learning Fundamentals.
Key Features
- Computation graph model that runs efficiently across CPUs, GPUs, and TPUs
- Eager execution by default in TensorFlow 2.x for intuitive, Python-like model building
- Keras as the official high-level API for defining and training neural networks
- tf.function decorator for compiling Python code into optimized, graph-based execution
- TensorFlow Lite for on-device inference on mobile and embedded hardware
- TensorFlow.js for training and running models directly in the browser or Node.js
- TensorFlow Serving and TensorFlow Extended (TFX) for production ML pipelines
- Broad ecosystem support across cloud providers and MLOps tooling
Use Cases
Frequently Asked Questions
From the Blog
PyTorch vs TensorFlow: Which to Learn in 2026
PyTorch wins for research and learning; TensorFlow/Keras wins for mobile and production deployment. Most beginners should start with PyTorch.
Read More Data ScienceTensorFlow and Keras: How the Two Fit Together
Keras is the model-building API and TensorFlow is the tensor runtime beneath it. Learn which layer each task belongs in — models and callbacks in Keras, data pipelines and graph compilation in TensorFlow — so shape errors, retracing surprises and deployment questions stop being confusing.
Read More