  We design a self size-estimating feed-forward network (SSFN) using a joint
optimization approach for estimation of number of layers, number of nodes and
learning of weight matrices. The learning algorithm has a low computational
complexity, preferably within few minutes using a laptop. In addition the
algorithm has a limited need for human intervention to tune parameters. SSFN
grows from a small-size network to a large-size network, guaranteeing a
monotonically non-increasing cost with addition of nodes and layers. The
learning approach uses judicious a combination of `lossless flow property' of
some activation functions, convex optimization and instance of random matrix.
Consistent performance -- low variation across Monte-Carlo trials -- is found
for inference performance (classification accuracy) and estimation of network
size.
