Artificial Neural Network Learning

Artificial Neural Network Learning - processes, mathematics and intuition

Mahesha Godekere NEURAL NETWORK

1. Overview

Artificial neural networks (ANNs) are a powerful class of models used for nonlinear regression and classification tasks inspired by biological neural computation. Significance of this notes is to give the clarity and completeness on the Neural Network Learning process and mathematics behind it!

2. Neural Network Learning

Neural Network consist of input, hidden, or output layers. There is only one input layer and one output layer but the number of hidden layers is unlimited. Neural Networks are feed-forward because nodes within a particular layer are connected only to nodes in the immediately downstream layer, so that nodes in the input layer activate only nodes in the subsequent hidden layer, which in turn activate only nodes in the next hidden layer, and so on until the nodes of the final hidden layer, which acts as output layer. This arrangement is illustrated in the three layered network with one hidden layer figure 1 below.

Let \( J(\theta) = 3\theta^2 + 2\)

Let \(\theta = 1$ and $\epsilon = 0.01\)

Use the formula to numerically compute an approximation to the derivative of \(\theta\) at \(theta = 1\)

\[ \[ \frac{J(\theta + \epsilon) - J(\theta + \epsilon)}{2\epsilon} \] \[ = \frac{(3(1.01)^2 + 2) - (3(0.99)^2 + 2)}{0.002} = 9.003 \] \]

Fig 1.Neural Network

The Neural Network Learning is divided into two phases:

a. Forward Propogation
b. Backpropogation

3. Forward Propogation

2. What is backpropogation?

Backpropagation is a method used in artificial neural networks to calculate a gradient (loss) used by the gradient descent optimization algorithm to adjust the weight of neurons. This technique is also called backward propagation of errors, because the error is calculated at the output and distributed back through the network layers.

2. Recap

The cost function for neural networks is a generic form of logistic regression function as below:

\( \begin{aligned} J(\Theta) = - \frac{1}{m} \sum_{i=1}^m \sum_{k=1}^K \left[y^{(i)}_k \log ((h_\Theta (x^{(i)}))_k) + (1 - y^{(i)}_k)\log (1 - (h_\Theta(x^{(i)}))_k)\right] + \frac{\lambda}{2m}\sum_{l=1}^{L-1} \sum_{i=1}^{s_l} \sum_{j=1}^{s_{l+1}} ( \Theta_{j,i}^{(l)})^2 \end{aligned} \tag{1}\) L = total number of layers in the network
l = number of units (not counting bias unit) in layer l
k = number of output units/classes
i = training set
\( h_\Theta(x)_k \) = hypothesis that results in the \( k^{th} \) output

  • The double sum simply adds up the logistic regression costs calculated for each cell in the output layer
  • The triple sum simply adds up the squares of all the individual Θs in the entire network
  • The i in the regularization triple sum does not refer to training example i

3. Backpropagation error \(\delta^{(l)}\)

To prove that error delta \(\delta^{(l)}\) of layer l is:

\[\delta^{(l)} = ((\Theta^{(l)})^T \delta^{(l+1)})\ .*\ a^{(l)}\ .*\ (1 - a^{(l)}) \tag{2}\]

4. The Delta Rule

Least Mean Square (LMS) method,

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