Practical overview
When it fits
Use it when you want notebook-based demonstrations of continual-learning methods in Google Colab, including examples tied to MNIST.
Who it is for
- Learners exploring continual-learning concepts through notebook demonstrations.
- Practitioners seeking examples of replay strategies, progressive neural networks or continual-learning exercises.
Documented uses
- Explore simple rehearsal and generative replay approaches.
- Study progressive neural networks and genetic pseudo rehearsal.
- Work through continual-learning examples using permuted and split MNIST.
- Inspect demonstrations of forgetting and an Avalanche example.
Requirements & limits
The repository instructions say to sign in to Google Colab before selecting a notebook. The repository is public and MIT-licensed.
- The documented workflow is built around Google Colaboratory.
- The repository combines introductory notebooks with method-specific and dataset-based demonstrations.
- The materials are notebooks and scripts, rather than a separate hosted course or service.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Open and run a project notebook in Google Colab
Use the repository’s documented Colab workflow to select and run one of its continual-learning notebooks.
Before you start
- A Google Colab account sign-in, as specified in the repository instructions.
Steps
Open Google Colab and sign in.
Choose the GitHub tab.
Search for “continualai”.
Pick a notebook and run it.
Expected outcome
A selected Colab notebook is opened for running as a continual-learning tutorial or demonstration.
Guide limitations
- The documented instructions cover selecting and running notebooks in Colab; they do not describe local setup or notebook-specific dependency requirements.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- ContinualAI/colab · Official homepage title · retained in its source language

