Mathematics Foundations

Mon, 01 Jan 0001 00:00:00 +0000

Four 3-hour sessions covering the linear algebra and calculus behind machine learning: vectors and cosine similarity, matrices as transformations and the forward pass, derivatives and the chain rule as backpropagation, and gradient descent.

The slides are made from my handwritten notes, taken live during class.

Sessions

Session 1: Vectors, Geometry & Similarity open ↗

Session 2: Matrices, Transformations & Eigenstructure open ↗

Session 3: Derivatives and the Chain Rule open ↗

Session 4: Gradients, Gradient Descent & Assessment open ↗

Session 1: vector addition and the dot product, cosine similarity on real data.
Session 2: matrices as linear transformations, the forward pass, eigenvectors and PCA.
Session 3: derivatives, differentiation rules, the chain rule as backpropagation.
Session 4: the gradient, gradient descent, local and global minima.

AlbertSchool | Etienne Chassaing

Mathematics Foundations

Sessions