This section contains documentation and usage instructions for many classes within GTSAM via Markdown files (*.md) and interactive Python notebooks (*.ipynb). Python notebooks with an 
button near the top can be opened in your browser, where you can run the files yourself and make edits to play with and understand GTSAM.
For troubleshooting singular, indefinite, or poorly conditioned factor graphs, see Debugging an Indeterminate Linear System.
To differentiate through GTSAM, for example to learn parameters of factors, see Gradients. For an end-to-end neural network example, see Learning odometry corrections with PyTorch.