Fast.ai v2 Released - What's New?
Blog post from Roboflow
Fastai v2, a significant update to the popular deep learning framework, introduces improvements to its library and offers new resources, including a machine learning course and helper repositories. Created by Jeremy Howard, Rachel Thomas, and Sylvain Gugger, fastai aims to democratize artificial intelligence by making it accessible and understandable to everyone through a layered architecture that allows users to perform complex AI tasks with minimal code. The new version enhances flexibility and ease of use, boasting a consistent experience across domains such as NLP and computer vision, and is built on top of PyTorch, incorporating updates to its functionality. Key features include an infinitely customizable training loop facilitated by callbacks and GPU-accelerated image augmentations. Additionally, fastai v2 introduces helper libraries like fastcore, fastscript, and fastgpu, which provide additional functionality and ease of use for Python developers. The release is complemented by a lecture series that explores foundational machine learning concepts and demonstrates the application of the new library.
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