Don't Panic — Machine Learning Frameworks

A machine learning framework is a library that handles tensor operations, gradient computation, and GPU dispatch so you can write model code instead of reimplementing backpropagation from scratch. That is the entire pitch. The rest is logistics.

The landscape splits into two tiers, and knowing which one your problem belongs to is the single decision that matters more than any framework choice. Classical ML (scikit-learn, XGBoost, LightGBM, CatBoost) covers tabular data — spreadsheets, CSVs, database rows. Deep learning (PyTorch, TensorFlow, JAX, Keras) covers unstructured data — images, text, audio, sequences. Most teams use one from each tier, and a surprising number of production ML systems run on the classical side. If your data fits in a table, the gradient boosting library that finished training in twelve minutes probably outperformed the neural network that took three days.

Here is the thing nobody tells you when they hand you a "framework comparison" chart: PyTorch won the research war so completely that the debate is mostly historical. In 2026, over 85 percent of deep learning papers at top conferences use PyTorch. TensorFlow's production ecosystem — Serving, Lite, TFX — is still real and still running on billions of devices, but the momentum belongs to the framework you can debug with pdb. If you are starting a new project and nobody has a strong opinion, learn PyTorch. If you already have a TFX pipeline running, stay where you are. The concepts transfer between frameworks; the migration tax does not.

The other surprise is that Hugging Face is not a framework. It is a model hub and a unified API that sits on top of PyTorch, TensorFlow, and JAX. When someone says "use Hugging Face," they mean "use a pretrained transformer model through a standardized interface." It is the single most important thing to know after you pick your deep learning framework, because it is where nearly every state-of-the-art model lands first.

What should you read next? The Don't Panic tab gave you the shape of the landscape. The Intro tab explains what each framework does and when to pick it. The Slides tab is the quick-reference decision map. The Cheatsheet has the API patterns you will copy-paste. The Landscape tab shows the actual products and services built around these frameworks.

Don't Panic — Machine Learning Frameworks

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