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<h1>Optimizers - Gradient-Based Parameter Updates<a class="headerlink" href="#optimizers-gradient-based-parameter-updates" title="Link to this heading">#</a></h1>
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<p>Welcome to the Optimizers module! This is where neural networks learn to improve through intelligent parameter updates.</p>
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<div class="tip admonition">
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<p class="admonition-title">🎯 Learning Goals</p>
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<ul class="simple">
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<li><p>Understand gradient descent and how optimizers use gradients to update parameters</p></li>
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<li><p>Implement SGD with momentum for accelerated convergence</p></li>
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<li><p>Build Adam optimizer with adaptive learning rates</p></li>
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<li><p>Master learning rate scheduling strategies</p></li>
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<li><p>See how optimizers enable effective neural network training</p></li>
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</ul>
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</div>
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<section id="build-use-analyze">
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<h2>Build → Use → Analyze<a class="headerlink" href="#build-use-analyze" title="Link to this heading">#</a></h2>
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<ol class="arabic simple">
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<li><p><strong>Build</strong>: Core optimization algorithms (SGD, Adam)</p></li>
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<li><p><strong>Use</strong>: Apply optimizers to train neural networks</p></li>
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<li><p><strong>Analyze</strong>: Compare optimizer behavior and convergence patterns</p></li>
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</ol>
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</section>
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⚡ Open in Colab</div>
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<a class="sd-stretched-link sd-hide-link-text reference external" href="https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/09_optimizers/optimizers_dev.ipynb"><span>https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/09_optimizers/optimizers_dev.ipynb</span></a></div>
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<a class="sd-stretched-link sd-hide-link-text reference external" href="https://github.com/mlsysbook/TinyTorch/blob/main/modules/source/09_optimizers/optimizers_dev.py"><span>https://github.com/mlsysbook/TinyTorch/blob/main/modules/source/09_optimizers/optimizers_dev.py</span></a></div>
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