| Samples | - | Visible | - |
| Highlighted | - | Hovered | - |
Display Options
Filter Classes
What is MNIST?
MNIST is a dataset of 70,000 handwritten digits (0-9), each represented as a 28x28 grayscale image (784 pixels).
Two Views
Raw Pixels: Project the 784-dim pixel values directly.
CNN Embeddings: Project 128-dim features learned by a neural network.
Training Evolution
In CNN mode, use the timeline to watch how embeddings evolve during training. The network learns to separate classes as it trains!
Interactions
- Hover - View the original image
- Click - Highlight all points of that class
- Filter - Toggle classes on/off
- Play - Animate through training epochs
PCA
Principal Component Analysis finds orthogonal axes of maximum variance. Fast and deterministic, but linear - it cannot capture non-linear relationships.
t-SNE
t-Distributed Stochastic Neighbor Embedding preserves local neighborhoods using probability distributions. Non-linear and excellent for visualization, but computationally expensive.
UMAP
Uniform Manifold Approximation and Projection uses topological methods. Faster than t-SNE and preserves both local and global structure better.
| Algorithm | Type | Speed | Structure |
|---|---|---|---|
| PCA | Linear | Fast | Global |
| t-SNE | Non-linear | Slow | Local |
| UMAP | Non-linear | Medium | Both |
Click a row to switch to that algorithm.