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@ -150,7 +150,7 @@ function activate(sum) {
return -1;
}
}</pre>
<h2 id="simple-pattern-recognition-using-a-perceptron">Simple Pattern Recognition Using a Perceptron</h2>
<h3 id="simple-pattern-recognition-using-a-perceptron">Simple Pattern Recognition Using a Perceptron</h3>
<p>Now that I have explained the computational process of a perceptron, let's take a look at an example of one in action. As I mentioned earlier, neural networks are commonly used for pattern recognition applications, such as facial recognition. Even simple perceptrons can demonstrate the fundamentals of classification. Let’s demonstrate with the following scenario.</p>
<figure class="half-width-right">
<img src="images/10_nn/10_nn_4.png" alt="Figure 10.4">
@ -197,7 +197,7 @@ function activate(sum) {
</tbody>
</table>
<p>The output is then the sum of the above three results: <span data-type="equation">0 + 0 + w_\text{bias}</span>. Therefore, the bias, by itself, answers the question of where <span data-type="equation">(0,0)</span> is in relation to the line. If the bias's weight is positive, then <span data-type="equation">(0,0)</span> is above the line; if negative, it is below. Its weight <strong>biases</strong> the perceptron's understanding of the line's position relative to <span data-type="equation">(0,0)</span>!</p>
<h2 id="coding-the-perceptron">Coding the Perceptron</h2>
<h3 id="coding-the-perceptron">Coding the Perceptron</h3>
<p>I am now ready to assemble the code for a <code>Perceptron</code> class. The perceptron only needs to track the input weights, which I can store using an array.</p>
<pre class="codesplit" data-code-language="javascript">class Perceptron {
constructor() {