6 docs tagged with "module-overview"

How Bayesian thinking transforms ML - uncertainty quantification, priors as regularization, probabilistic programming, and principled model comparison. Module map and learning objectives.

A complete module map showing how derivatives, gradients, backpropagation, gradient descent, and optimization algorithms connect to training every major ML model.

A complete module map showing how vectors, matrices, eigenvalues, SVD, and tensors connect to every major ML algorithm - from attention to PCA to backpropagation.

A comprehensive module on computer vision covering CNNs, modern architectures, object detection, segmentation, data augmentation, and Vision Transformers using PyTorch.

The mathematical theory of generalization - why ML models work, when they fail, and how to bound their error. Module map and learning objectives for PAC learning, VC dimension, and modern generalization theory.

How statistical theory powers ML model evaluation, A/B testing, and production AI systems. Module map, prerequisites, and learning objectives.