### Notation

Notation used in the slides:

**N** - number of training points

**x**_{n} - indexed training point

**Q** - input dimensionality

**D** - output dimensionality

**C** - number of classes in classification

**K** - number of units (neurons), often in last layer

**X** - training inputs (N by Q matrix)

**x** - single training input (Q by 1 vector)

**y** - single training output (D by 1 vector)

**D = {(x**_{1}, y_{1}), ..,(x_{N}, y_{N})} = X, Y - training set

**W** - neural network weight matrix (often last layer, often a random variable)

**theta** - variational parameters