Vectors & Dot Products
Feature vectors, vector arithmetic, and dot products with weights that translate real-world attributes into raw decision scores for neural networks.
Lessons in this Topic
Feature Vectors
Ordered numeric lists and arrays that establish mathematical identity profiles for real-world attributes in neural network layers.
Vector Addition and Scaling
Position-wise vector addition and scalar multiplication that combine feature profiles, scale attribute values, and combine multidimensional data.
Dot Products and Weights
Pairwise feature-weight multiplication and summation that accumulate attribute influences into single scalar decision scores in neural network layers.
From Vectors to Neural Networks
The bridge from raw dot products to artificial neurons, exposing uncalibrated score scales and motivating baseline bias offsets and activation functions.
Vectors and Dot Products In Practice
Manual problem sets covering vector construction, position-wise arithmetic, and dot products with weights in abstract and applied decision settings.