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    <title>DEV Community: zaina-ml</title>
    <description>The latest articles on DEV Community by zaina-ml (@zainaml).</description>
    <link>https://dev.to/zainaml</link>
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      <title>DEV Community: zaina-ml</title>
      <link>https://dev.to/zainaml</link>
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    <item>
      <title>I visually created and trained a CNN in 2 minutes, with no code.</title>
      <dc:creator>zaina-ml</dc:creator>
      <pubDate>Fri, 03 Apr 2026 00:16:48 +0000</pubDate>
      <link>https://dev.to/zainaml/i-visually-created-and-trained-a-cnn-in-2-minutes-with-no-code-1hce</link>
      <guid>https://dev.to/zainaml/i-visually-created-and-trained-a-cnn-in-2-minutes-with-no-code-1hce</guid>
      <description>&lt;h3&gt;
  
  
  For those who are tired of writing the same ML boilerplate every single time or to beginners who don't have coding experience.
&lt;/h3&gt;

&lt;p&gt;MLForge is an app that lets you visually craft a machine learning pipeline.&lt;/p&gt;

&lt;p&gt;You build your pipeline like a node graph across three tabs:&lt;/p&gt;

&lt;p&gt;Data Prep - drag in a dataset (MNIST, CIFAR10, etc), chain transforms, end with a DataLoader. Add a second chain with a val DataLoader for proper validation splits.&lt;/p&gt;

&lt;p&gt;Model - connect layers visually. Input -&amp;gt; Linear -&amp;gt; ReLU -&amp;gt; Output. A few things that make this less painful than it sounds:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Drop in a MNIST (or any dataset) node and the Input shape auto-fills to 1, 28, 28&lt;/li&gt;
&lt;li&gt;Connect layers and in_channels / in_features propagate automatically&lt;/li&gt;
&lt;li&gt;After a Flatten, the next Linear's in_features is calculated from the conv stack above it, so no more manually doing that math&lt;/li&gt;
&lt;li&gt;Robust error checking system that tries its best to prevent shape errors.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Training - Drop in your model and data node, wire them to the Loss and Optimizer node, press RUN. Watch loss curves update live, saves best checkpoint automatically.&lt;/p&gt;

&lt;p&gt;Inference - Open up the inference window where you can drop in your checkpoints and evaluate your model on test data.&lt;/p&gt;

&lt;p&gt;Pytorch Export - After your done with your project, you have the option of exporting your project into pure PyTorch, just a standalone file that you can run and experiment with.&lt;/p&gt;

&lt;p&gt;Free, open source. Project showcase is on README in Github repo.&lt;/p&gt;

&lt;p&gt;GitHub: &lt;a href="https://github.com/zaina-ml/ml_forge" rel="noopener noreferrer"&gt;https://github.com/zaina-ml/ml_forge&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To install MLForge, enter the following in your command prompt&lt;/p&gt;

&lt;p&gt;&lt;code&gt;pip install zaina-ml-forge&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Then&lt;/p&gt;

&lt;p&gt;&lt;code&gt;ml-forge&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Please, if you have any feedback feel free to comment it below. My goal is to make this software that can be used by beginners and pros.&lt;/p&gt;

&lt;p&gt;This is v1.0 so there will be rough edges, if you find one, drop it in the comments and I'll fix it.&lt;/p&gt;

</description>
      <category>ai</category>
      <category>nocode</category>
      <category>machinelearning</category>
      <category>lowcode</category>
    </item>
    <item>
      <title>I built a visual drag-and-drop ML trainer (no code required). Free &amp; open source.</title>
      <dc:creator>zaina-ml</dc:creator>
      <pubDate>Tue, 17 Mar 2026 22:17:17 +0000</pubDate>
      <link>https://dev.to/zainaml/i-built-a-visual-drag-and-drop-ml-trainer-no-code-required-free-open-source-3e50</link>
      <guid>https://dev.to/zainaml/i-built-a-visual-drag-and-drop-ml-trainer-no-code-required-free-open-source-3e50</guid>
      <description>&lt;h3&gt;
  
  
  For those who are tired of writing the same ML boilerplate every single time or to beginners who don't have coding experience.
&lt;/h3&gt;

&lt;p&gt;MLForge is an app that lets you visually craft a machine learning pipeline.&lt;/p&gt;

&lt;p&gt;You build your pipeline like a node graph across three tabs:&lt;/p&gt;

&lt;p&gt;Data Prep - drag in a dataset (MNIST, CIFAR10, etc), chain transforms, end with a DataLoader. Add a second chain with a val DataLoader for proper validation splits.&lt;/p&gt;

&lt;p&gt;Model - connect layers visually. Input -&amp;gt; Linear -&amp;gt; ReLU -&amp;gt; Output. A few things that make this less painful than it sounds:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Drop in a MNIST (or any dataset) node and the Input shape auto-fills to 1, 28, 28&lt;/li&gt;
&lt;li&gt;Connect layers and in_channels / in_features propagate automatically&lt;/li&gt;
&lt;li&gt;After a Flatten, the next Linear's in_features is calculated from the conv stack above it, so no more manually doing that math&lt;/li&gt;
&lt;li&gt;Robust error checking system that tries its best to prevent shape errors.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Training - Drop in your model and data node, wire them to the Loss and Optimizer node, press RUN. Watch loss curves update live, saves best checkpoint automatically.&lt;/p&gt;

&lt;p&gt;Inference - Open up the inference window where you can drop in your checkpoints and evaluate your model on test data.&lt;/p&gt;

&lt;p&gt;Pytorch Export - After your done with your project, you have the option of exporting your project into pure PyTorch, just a standalone file that you can run and experiment with.&lt;/p&gt;

&lt;p&gt;Free, open source. Project showcase is on README in Github repo.&lt;/p&gt;

&lt;p&gt;GitHub: &lt;a href="https://github.com/zaina-ml/ml_forge" rel="noopener noreferrer"&gt;https://github.com/zaina-ml/ml_forge&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To install MLForge, enter the following in your command prompt&lt;/p&gt;

&lt;p&gt;&lt;code&gt;pip install zaina-ml-forge&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Then&lt;/p&gt;

&lt;p&gt;&lt;code&gt;ml-forge&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Please, if you have any feedback feel free to comment it below. My goal is to make this software that can be used by beginners and pros.&lt;/p&gt;

&lt;p&gt;This is v1.0 so there will be rough edges, if you find one, drop it in the comments and I'll fix it.&lt;/p&gt;

</description>
      <category>ai</category>
      <category>beginners</category>
      <category>machinelearning</category>
      <category>python</category>
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