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	<title>10.4.2 Activation Functions - Revision history</title>
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	<updated>2026-06-15T08:19:27Z</updated>
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		<id>https://wiki.omnivision.website/index.php?title=10.4.2_Activation_Functions&amp;diff=192&amp;oldid=prev</id>
		<title>Mr. Goldstein: Created page with &quot;=== 10.4.2 Activation Functions === After a perceptron does its math, it needs to decide whether to &quot;fire&quot; or &quot;activate&quot; and send a signal to the next layer. This is where &#039;&#039;&#039;activation functions&#039;&#039;&#039; come in!  Think of it like a light switch. If the total &quot;strength&quot; of the incoming signals is strong enough, the switch turns on, and the perceptron sends a signal forward. If it&#039;s not strong enough, the switch stays off.  Activation functions are mathematical rules that help...&quot;</title>
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		<updated>2025-07-08T19:55:15Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;=== 10.4.2 Activation Functions === After a perceptron does its math, it needs to decide whether to &amp;quot;fire&amp;quot; or &amp;quot;activate&amp;quot; and send a signal to the next layer. This is where &amp;#039;&amp;#039;&amp;#039;activation functions&amp;#039;&amp;#039;&amp;#039; come in!  Think of it like a light switch. If the total &amp;quot;strength&amp;quot; of the incoming signals is strong enough, the switch turns on, and the perceptron sends a signal forward. If it&amp;#039;s not strong enough, the switch stays off.  Activation functions are mathematical rules that help...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;=== 10.4.2 Activation Functions ===&lt;br /&gt;
After a perceptron does its math, it needs to decide whether to &amp;quot;fire&amp;quot; or &amp;quot;activate&amp;quot; and send a signal to the next layer. This is where &amp;#039;&amp;#039;&amp;#039;activation functions&amp;#039;&amp;#039;&amp;#039; come in!&lt;br /&gt;
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Think of it like a light switch. If the total &amp;quot;strength&amp;quot; of the incoming signals is strong enough, the switch turns on, and the perceptron sends a signal forward. If it&amp;#039;s not strong enough, the switch stays off.&lt;br /&gt;
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Activation functions are mathematical rules that help the perceptron make this &amp;quot;on or off&amp;quot; or &amp;quot;how much signal to send&amp;quot; decision. They introduce non-linearity, which means the network can learn more complex patterns than if it just did simple addition. Without them, even a deep network would just be doing simple multiplication and addition, like a straight line. With them, it can learn curves and complex shapes in the data.&lt;br /&gt;
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&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;Bibliography&amp;#039;&amp;#039;&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
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==== 10.4.2 Activation Functions ====&lt;br /&gt;
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* &amp;#039;&amp;#039;&amp;#039;What are Activation Functions? (Simple)&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
** [https://encord.com/blog/activation-functions-neural-networks/ An easy-to-understand guide on why activation functions are important].&lt;/div&gt;</summary>
		<author><name>Mr. Goldstein</name></author>
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