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<!DOCTYPE html>
<html lang="en" dir="ltr">
<head>
<meta charset="utf-8">
<link href="https://fonts.googleapis.com/css?family=Roboto" rel="stylesheet">
<link href="https://fonts.googleapis.com/css?family=Gudea|Hind+Madurai|Oxygen:300|Rubik" rel="stylesheet">
<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.1.1/css/all.css" integrity="sha384-O8whS3fhG2OnA5Kas0Y9l3cfpmYjapjI0E4theH4iuMD+pLhbf6JI0jIMfYcK3yZ" crossorigin="anonymous">
<link rel="stylesheet" href="./css/demo.css">
<!-- <link rel="stylesheet" href="./css/neuron_demo.css"> -->
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-133914635-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-133914635-1');
</script>
<title>Project SynGap - Demo</title>
</head>
<body>
<div class="scrollBar" id="myBar"></div>
<div id="brainText" class="firstScene">
<div class="text right">
<h2>An Interactive <br> Introduction to <br> Artificial Intelligence</h2>
<hr>
<p><strong>Artificial neural network</strong> is a type of machine learning algorithm inspired by the human brain.</p>
<p>Our brains are comprised of a network of biological neurons that fire signals upon receiving stimulus, ultimately telling our bodies what function to perform.</p>
<p style="text-align: center"> <i>Scroll</i> <br> <i class="fas fa-angle-down fa-2x"></i></p>
</div>
</div>
<div id="nerveText" class="scene">
<div class="text right">
<p>At a more <strong>microscopic</strong> level, the dendrites of a single neuron in the brain receives inputs,
and fire output signals via the axon to the dendrites of other neurons, and so on.
</p>
</div>
</div>
<div id="neuron1Text" class="scene bg-grey upperLayer">
<div class="text left">
<h3>
From Biological to Artificial
</h3>
<hr>
<p>An <strong>artificial neuron</strong> models a biological neuron in a simplified manner. <br>
Here, the previous biological nerve system is represented by an artificial neuron with two inputs and a output.
</p>
</div>
</div>
<div id="neuron2Text" class="scene bg-grey upperLayer">
<div class="text left">
<p>Between two neurons, the biological axon-to-dendrite connection is simplified to an <strong>edge</strong>, and
signals transmitted are represented with <strong>numbers</strong>.
</p>
<p>The input on top is connected to the central neuron with a <strong>thick</strong> edge,
thus the signal is amplified by a factor of 2 when it reaches the neuron.
</p>
</div>
</div>
<div id="neuron3Text" class="scene bg-grey upperLayer">
<div class="text left">
<p>The input at the bottom, however, has a <strong>thinner</strong> connection to the neuron,
thus the signal is reduced to 2, resulting in a final signal of <strong>2 + 2 = 4</strong> received, instead of
3 + 2 = 5.
</p>
<p>
The thickness of the edge is called <strong>weight</strong> in machine learning.
</p>
</div>
</div>
<div id="neuron4Text" class="scene bg-grey upperLayer">
<div class="text left">
<p>Next, the neuron processes the signal by shaking itself. In this case, doing so incremented the signal by 1.
</p>
<p>
The “shakiness” of a neuron is called <strong>bias</strong>, which can both increase or decrease the value of the signal by any set amount.
</p>
</div>
</div>
<div id="neuron5Text" class="scene bg-grey upperLayer">
<div class="text left">
<p>Finally, an <strong>activation function</strong> is used to simulate how "fired" the neuron is.</p>
<p>
<strong>ReLU</strong>, a type of activation functions, is used here. It preserves the signal value if it is 0 or greater.
</p>
<p>
In our case, a positive signal would fully fire the neuron, so the value 5 is preserved and becomes the final output of the neuron.
</p>
</div>
</div>
<div id="appanana1Text" class="scene topLayer bg-white">
<div class="text right">
<h3>
Apple? Banana?
</h3>
<hr>
<p>
Using an artificial neuron with two inputs, we can build the simplest neural network: <strong> perceptron</strong>.
</p>
<p>
We are going to use it to distinguish between apple and banana. To put it more formally, perceptron can do
<strong>binary classification</strong>.
</p>
</div>
</div>
<div id="appanana2Text" class="scene topLayer bg-white">
<div class="text right">
<p>
To simplify the matter, let’s assume that only two characteristics differs between apple and banana: <strong>color and shape</strong>.
</p>
<p>
Apple is red and spherical, and banana is yellow and cylindrical.
</p>
</div>
</div>
<div id="appanana3Text" class="scene topLayer bg-white">
<div class="text right">
<p>
The perceptron here receives two signals. The input on top receives a <strong>"sphere"</strong> signal,
and the one at the bottom receives a <strong>"red"</strong> signal.
</p>
<p>
These signals are sent to the artificial neuron in the center to be processed.
</p>
</div>
</div>
<div id="appanana4Text" class="scene topLayer bg-white">
<div class="text right">
<p>
The artificial neuron shakes itself and processes the signal. It then outputs the signal to show the final result:
an <strong>apple</strong>.
</p>
</div>
</div>
<div id="appanana5Text" class="scene topLayer bg-white">
<div class="text right">
<p>
Similarly, a combination of <strong>"cylinder"</strong> and <strong>"yellow"</strong> signals are being input into the perceptron.
</p>
<p>
These signals are again sent to the artificial neuron in the center to be processed.
</p>
</div>
</div>
<div id="appanana6Text" class="scene topLayer bg-white">
<div class="text right">
<p>
The result of this combination of signal is a <strong>banana</strong>!
</p>
<p>
In the next chapter, we are going to take a look at <strong>how perceptrons can learn and distinguish</strong> between
appple and banana behind the scenes.
</p>
</div>
</div>
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