computer-vision-in-action

Code examples are provided as Jupyter notebooks that can run instantly on Binder or Google Colab, removing local setup barriers, as highlighted by the badges and instructions in the README.

Covers a wide range from neural networks and CNNs to advanced topics like transformers and GANs, structured into theory, practice, and advanced sections, as detailed in the extensive table of contents.

Includes practical projects such as lane detection, image stitching, and style transfer, emphasizing the 'learning by doing' philosophy stated in the README to make concepts accessible.

The L0CV Python package simplifies code reuse and imports, as mentioned in the Key Features, aiding consistency across examples and reducing boilerplate code for learners.