Neural Network explained

Week 4 & 5 of the Machine Learning course delve into Neural Network Model.


A Neuron Model contains a logistic unit that produces output based on the given inputs. Typically, a bias unit is included and it is set to 1. Note that the hypothesis used is based on the Logistic Regression Model.

The Neural Network Model consists of "Input" layer, "Hidden" layer and "Output" layer.


"Input" layer
Number of nodes = Number of features

"Hidden" layer
It is made up of 1 or more "Activation" units.

"Output" layer
Number of nodes = Number of classes


More "Hidden" layers can be added to achieve better results. However, this results in higher computational costs.

Random initialization is necessary for the initial value of theta (i.e. the "weights" of a specific "Activation" unit for each input). This breaks the symmetry of the matrix so that all the values of the "Activation" units will be different.

By applying Forward Propagation to the Neural Network, we can obtain a hypothesis using the sigmoid / logistic function.


Cost function J(Ө) is not convex and thus we can end up in a local minimum instead but it's not a big problem. Backpropagation algorithm is applied to find the optimal gradient (Ө) that results in minimum cost (i.e. the hypothesis ≈ actual output value).


In some cases, even though the Cost function J(Ө) is at its minimum, the hypothesis may contain errors that are not easily detectable. Therefore, it is important to check the gradient produced by the Backpropagation algorithm. Tip: Since gradient checking involve large computation power, disable it after checking the Backpropagation algorithm.


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