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Awni Hannun
2023-12-06 08:13:20 -08:00
committed by CircleCI Docs
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commit f89de9c25d
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docs/build/html/examples/mlp.html vendored
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@@ -9,7 +9,7 @@
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
<title>Multi-Layer Perceptron &#8212; MLX 0.0.0 documentation</title>
<title>Multi-Layer Perceptron &#8212; MLX 0.0.3 documentation</title>
@@ -134,8 +134,8 @@
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.0.0 documentation - Home"/>
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.0.0 documentation - Home"/>`);</script>
<img src="../_static/mlx_logo.png" class="logo__image only-light" alt="MLX 0.0.3 documentation - Home"/>
<script>document.write(`<img src="../_static/mlx_logo.png" class="logo__image only-dark" alt="MLX 0.0.3 documentation - Home"/>`);</script>
</a></div>
@@ -647,7 +647,7 @@ the gradient of a loss with respect to the trainable parameters of a model.
This should not be confused with <a class="reference internal" href="../python/_autosummary/mlx.core.value_and_grad.html#mlx.core.value_and_grad" title="mlx.core.value_and_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">mlx.core.value_and_grad()</span></code></a>.</p>
</div>
<p>The model should train to a decent accuracy (about 95%) after just a few passes
over the training set. The <a class="reference external" href="https://github.com/ml-explore/mlx-examples/tree/main/mlp">full example</a>
over the training set. The <a class="reference external" href="https://github.com/ml-explore/mlx-examples/tree/main/mnist">full example</a>
is available in the MLX GitHub repo.</p>
</section>