fastai is an open-source Python library for deep learning built on top of PyTorch. It bundles proven default settings so that models for images, text or tabular data can be trained with a few lines of code, while still allowing deep customisation at every level. It grew out of the fast.ai learning initiative and accompanies its free courses, which is why it is popular in education and for quickly trying out models.
PyTorch is an open-source framework for machine learning and deep learning in the Python programming language. It was originally developed at Meta (then Facebook) and has been stewarded since 2022 by the PyTorch Foundation under the umbrella of the Linux Foundation. PyTorch is widely used in research and industry because it lets developers build models flexibly, train them on GPUs and test them step by step; many well-known language and image models are built with it.
Deep learning is a branch of machine learning that uses artificial neural networks with many layers. Such networks learn from large amounts of data which features matter – from edges in images to relationships of meaning in texts. Deep learning underpins today's language models, image and speech recognition and machine translation, and usually requires considerable computing power and training data.
In transfer learning, a model that has already learnt a large general task serves as the starting point for a new, specialised one. Instead of starting from scratch, it is adapted, for instance through fine-tuning, using comparatively little data of your own. This saves computing time and data, for example when a general image model is meant to learn to spot production defects in photos.
Keras is an open-source Python library for building and training neural networks, originally developed by François Chollet. It offers an accessible, high-level interface for assembling models from layers. Since version 3, Keras can run on JAX, TensorFlow or PyTorch as its computational backend. Example: a team builds a prototype image classification model in just a few lines of code.
