Hands-On Deep Learning with Go

The structure of a neural network

The purpose of this example is clearly not to build a cutting-edge computer vision system but, rather, to demonstrate how to use these fundamental operations (and how Gorgonia handles them) in the context of a parameterized function where the parameters are learned over time. The key goal of this section is to understand the idea of a network that learns. This learning really just means the continuous, deliberate re-parameterization of the network (updating the weights). This is done by an optimization method that is, essentially, a small amount of code representing some basic undergraduate-level calculus.

The Sigmoid function (and activation functions more generally), Stochastic Gradient Descent (SGD), and ...