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‎.DS_Store‎

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‎docs/activation.html‎

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<meta name="Description" content="Building neural networks from scratch using Java.">
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<title>Activation Functions & Derivatives</title>
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<!-- Math -->
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function \(f(x)\). It is not exactly necessary for you to have a very deep understanding of derivatives until we get to the topic of <em>backpropagation</em>, but I have explained it
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now in case you were curious. If you are a middle school or high school student, you probably won’t have been in contact with derivatives or calculus as a whole, and it is not
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necessary for you to learn these topics before being taught in school. Instead, just follow my explanations and see if you can understand it. If not, please submit a
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<a href="https://docs.google.com/forms/d/e/1FAIpQLSc-JrIBxMqlcOyLN6rgmhH5AVhNEj7LlJPhQTQk9zrbYG4Uhg/viewform" target="_blank">feedback form</a> or ask/learn about the topics online,
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on platforms like <a href="https://khanacademy.org/" target="_blank">Khan Academy</a> if you are curious.</p>
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<a rel="noopener" href="https://docs.google.com/forms/d/e/1FAIpQLSc-JrIBxMqlcOyLN6rgmhH5AVhNEj7LlJPhQTQk9zrbYG4Uhg/viewform" target="_blank">feedback form</a> or ask/learn about the topics online,
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on platforms like <a rel="noopener" href="https://khanacademy.org/" target="_blank">Khan Academy</a> if you are curious.</p>
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<p>It is almost time to start training our own neural network, so head on to the next chapter!</p>
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<h3 id="summary">Chapter Summary</h3>

‎docs/book.css‎

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‎docs/content.html‎

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<meta name="Description" content="Building neural networks from scratch using Java.">
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<title>Contents</title>
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‎docs/cover.html‎

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