MOD-A
Linear Algebra
The engine of deep learning. Spaces, transforms, and tensors.
Stop treating Neural Networks like magic. Visualize the linear algebra, calculus, and optimization dynamics that power modern AI.
Select a computational module to inspect.
The engine of deep learning. Spaces, transforms, and tensors.
Understanding change. Gradients, derivatives, and backprop.
The mechanics of learning. Activation, loss, and propagation.
Finding the minimum. Stochastic methods and hyperparams.
Structural patterns. CNNs, Transformers, and RNNs.
Observing convergence. Loss landscapes and gradient flow.
Interactive experiments. Test your intuition in real-time.
Cutting-edge techniques. Style transfer and ensemble methods.
Foundations of uncertainty. Distributions and inference.