Tutorials
These tutorials will help you get familiar with Monty. Topics and experiments increase in complexity as the tutorials progress, so we recommend going through them in order. You can also learn about contributing tutorials here.
Running your first experiment
In this tutorial we will introduce the basic mechanics of Monty experiment configs, how to run them, and what happens during the execution of a Monty experiment.
Pretraining a model
This tutorial demonstrates how to configure and run Monty experiments for pretraining.
Running inference with a pretrained model
This tutorial is a follow-up of our tutorial on pretraining a model. Here we will load the pretraining data into the model and perform object recognition under noisy conditions and several arbitrary object rotations.
Unsupervised continual learning
This tutorial demonstrates how to configure and run Monty experiments for unsupervised continual learning. In this regime, Monty learns while it explores an object and attempts to identify its identity and pose.
Multiple learning modules
In this tutorial, we will show how Monty can be used to learn and recognize objects in a multiple sensor, multiple learning module setting. In this regime, we can perform object recognition with fewer steps than single-LM systems by allowing learning modules to communicate with one another through a process called voting.