# Deep Learning Notation - Single training example: `(x,y)`. Where `x` is an x-dimensional feature vector and `y` is 0 or 1. - The training set will include `n` training examples - x(1), y(1) - ... - x(n), y(n) - Sometimes we will also have `m` test examples - The matrix `X` will be defined by taking the training set inputs `x1`, `x2`, ... and stacking them as columns in that matrix - ![[20230822165630 matrix.png]] - That matrix has - `m` columns (number of training examples) - `nx` rows - The shape of that matrix is `X = (nx,m)` - Sometimes that matrix is transposed (column instead of row), but the representation above is easier to implement - There is also a `Y = [y1, y2, ..., ym]` matrix - It is a `1 x m` dimensional matrix - The shape of that matrix is `Y = (1, m)`