Please choose whether you would like to figure out the exercises for yourself or follow along with me.
When you open any of these notebooks on Colab, make sure that you choose a GPU instance by changing the runtime with the button on the top right.
For an introductory coding tutorial, access Intro Coding Tutorial:
For working on your own click here to access Binary Classification Exercise:
For following along with me, access Binary Classification ReadAlong:
For a more introductory Python Tutorial, access Python Introduction:
If you're finished with the exercises and want to take a break, you can check out some fun demos of larger machine learning models:
- FlyVis tutorial: using the model, a walkthrough of running the FlyVis connectome-constrained visual system model.
- FlyVis tutorial: mechanism discovery, digging into how the FlyVis model's mechanisms can be analyzed and interpreted.
- TabPFN, a foundation model for tabular data.
- DINOv3, a foundation model for visual data.
- Segment Anything 2, a segmentation and object tracking model for images and video. First check out the web demo, and then check out the notebook to find out how to run the model yourself with code and track more than 3 objects in a video, unlike the limited web demo.
- Super-resolution with latent diffusion. This model takes a low resolution image and upscales it to get a sharp high resolution image.
- Shap-E, a 3D generative text to 3D model.
- Kandinsky diffusion, an open source text to image generation model.
- Stable diffusion, an open source text to image generation model.