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<title>Coder Legion - Recent posts tagged predict</title>
<link>https://coderlegion.com/tag/predict</link>
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<title>Introduction to Neural Networks</title>
<link>https://coderlegion.com/18993/introduction-to-neural-networks</link>
<description>
&lt;p&gt;Hello, I'm Ganesh. I'm building &lt;em&gt;git-lrc&lt;/em&gt;, a free, micro AI code review that runs on commit. It is free, and source-available on GitHub. &lt;a href=&quot;https://github.com/HexmosTech/git-lrc&quot; rel=&quot;noopener noreferrer nofollow&quot;&gt;Star Us&lt;/a&gt; to help devs discover the project. Do give it a try and share your feedback for improving the product.&lt;/p&gt;

&lt;p&gt;In this series of articles, we will explore the world of neural networks and how they work.&lt;/p&gt;

&lt;p&gt;In this article, we will discuss the fundamental idea of neural networks.&lt;/p&gt;

&lt;h2&gt;
  
  
  What is a Neural Network?
&lt;/h2&gt;

&lt;p&gt;A neural network is a computational model that is inspired by the structure and function of the human brain.&lt;/p&gt;

&lt;p&gt;It is widely used in many fields like image recognition, natural language processing, etc.&lt;br&gt;
It is most popular machine learning algorithm.&lt;/p&gt;

&lt;p&gt;Simplest image of a Neural Network is : &lt;br&gt;
&lt;a rel=&quot;nofollow&quot; href=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fhaprkizrj1f1xwo02kkj.png&quot; class=&quot;article-body-image-wrapper&quot;&gt;&lt;img src=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fhaprkizrj1f1xwo02kkj.png&quot; alt=&quot;&quot; width=&quot;330&quot; height=&quot;397&quot;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  What Neural Networks Manily Do?
&lt;/h2&gt;

&lt;p&gt;It tries to approximate the function of the given input and map it to the output.&lt;/p&gt;

&lt;p&gt;In simple terms, it tries to learn the relationship between the input and output. It approximates the output based on the input data.&lt;/p&gt;

&lt;p&gt;This will be be done with basic mathematical functions which totaly changes based on the data we provided.&lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;p&gt;Let's take example of Drug which can cure illness.&lt;/p&gt;

&lt;p&gt;Now let's split the gruop of people based on dosage of drug they consume.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Low dosage: Group A&lt;/li&gt;
&lt;li&gt;Medium dosage: Group B&lt;/li&gt;
&lt;li&gt;High dosage: Group C&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Most Commonly Gruop results may be ploted as this in the graph.&lt;/p&gt;

&lt;p&gt;&lt;a rel=&quot;nofollow&quot; href=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Ftg23g1z0ntul0hi07cs1.png&quot; class=&quot;article-body-image-wrapper&quot;&gt;&lt;img src=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Ftg23g1z0ntul0hi07cs1.png&quot; alt=&quot;&quot; width=&quot;800&quot; height=&quot;667&quot;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To predict the efficacy of the dosage of drug we can't simply use simple straight line or normal distribution.&lt;/p&gt;

&lt;p&gt;As for example I ploted this graph for understaning on how it is done.&lt;/p&gt;

&lt;p&gt;&lt;a rel=&quot;nofollow&quot; href=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5olh58w96ga2ebnxc943.png&quot; class=&quot;article-body-image-wrapper&quot;&gt;&lt;img src=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F5olh58w96ga2ebnxc943.png&quot; alt=&quot;&quot; width=&quot;800&quot; height=&quot;667&quot;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is where nerual network will help.&lt;/p&gt;

&lt;p&gt;If we have data points in different location it will find and draw a line to predict the values between the points.&lt;/p&gt;

&lt;p&gt;&lt;a rel=&quot;nofollow&quot; href=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fgjwhimdcs5lawom60bb7.png&quot; class=&quot;article-body-image-wrapper&quot;&gt;&lt;img src=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fgjwhimdcs5lawom60bb7.png&quot; alt=&quot;&quot; width=&quot;800&quot; height=&quot;667&quot;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;By above graph we can able to understand what neural network can do.&lt;/p&gt;

&lt;h2&gt;
  
  
  Conclusion
&lt;/h2&gt;

&lt;p&gt;Neural Network is the best tool to fit the data points which are scattered with curve which we can use it to predict the values in between the data points.&lt;/p&gt;

&lt;p&gt;We got to know with basic example what it actualy do.&lt;/p&gt;

&lt;p&gt;&lt;a href=&quot;https://github.com/HexmosTech/git-lrc&quot; rel=&quot;noopener noreferrer nofollow&quot;&gt;&lt;img src=&quot;https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fabn6nl3iehrohehnxgje.png&quot; alt=&quot;git-lrc&quot; width=&quot;800&quot; height=&quot;373&quot;&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Any feedback or contributors are welcome! It&amp;rsquo;s online, source-available, and ready for anyone to use. &lt;br&gt;
&amp;#11088; Star it on GitHub: &lt;a href=&quot;https://github.com/HexmosTech/git-lrc&quot; rel=&quot;noopener noreferrer nofollow&quot;&gt;https://github.com/HexmosTech/git-lrc&lt;/a&gt;&lt;/p&gt;


</description>
<category>Other</category>
<guid isPermaLink="true">https://coderlegion.com/18993/introduction-to-neural-networks</guid>
<pubDate>Sun, 31 May 2026 18:12:12 +0000</pubDate>
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