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Fixed typo. #197

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Fixed typo. #197

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7182522
Fixed a typo.
anthropikos Mar 13, 2023
3e8c146
Updated typo
anthropikos Mar 13, 2023
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4 changes: 2 additions & 2 deletions docs/Module3_IntroducingNumpy/AutoDiff.html
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Expand Up @@ -439,7 +439,7 @@ <h2>Introduction to MyGrad<a class="headerlink" href="#Introduction-to-MyGrad" t
</pre></div>
</div>
<p>As expected, MyGrad computes the appropriate value for the evaluated derivative: <span class="math notranslate nohighlight">\(\frac{\mathrm{d}f}{\mathrm{d}x}\big|_{x=5}=2 \times 5=10\)</span>. Note that all <code class="docutils literal notranslate"><span class="pre">Tensor</span></code> instances have a <code class="docutils literal notranslate"><span class="pre">grad</span></code> attribute, but prior to invoking <code class="docutils literal notranslate"><span class="pre">fx.backward()</span></code>, <code class="docutils literal notranslate"><span class="pre">x.grad</span></code> would have simply returned <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p>
<p>It is important to reiterate that MyGrad <em>never gives us the actual function</em> <span class="math notranslate nohighlight">\(\frac{\mathrm{d}f}{\mathrm{d}x}\)</span>; it only computes the derivative evaluated at a specific input <span class="math notranslate nohighlight">\(x=10\)</span>.</p>
<p>It is important to reiterate that MyGrad <em>never gives us the actual function</em> <span class="math notranslate nohighlight">\(\frac{\mathrm{d}f}{\mathrm{d}x}\)</span>; it only computes the derivative evaluated at a specific input <span class="math notranslate nohighlight">\(x=5\)</span>.</p>
<div class="section" id="MyGrad-Adds-“Drop-In”-AutoDiff-to-NumPy">
<h3>MyGrad Adds “Drop-In” AutoDiff to NumPy<a class="headerlink" href="#MyGrad-Adds-“Drop-In”-AutoDiff-to-NumPy" title="Permalink to this headline"></a></h3>
<p>MyGrad’s functions are intentionally designed to mirror NumPy’s functions almost exactly. In fact, for all of the NumPy functions that MyGrad mirrors, we can pass a tensor to a NumPy function and it will be “coerced” into returning a tensor instead of a NumPy array – thus we can differentiate through NumPy functions!</p>
Expand Down Expand Up @@ -840,4 +840,4 @@ <h2>Reading Comprehension Exercise Solutions<a class="headerlink" href="#Reading
</script>

</body>
</html>
</html>
2 changes: 1 addition & 1 deletion docs/Module3_IntroducingNumpy/AutoDiff.ipynb
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Expand Up @@ -142,7 +142,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"It is important to reiterate that MyGrad *never gives us the actual function* $\\frac{\\mathrm{d}f}{\\mathrm{d}x}$; it only computes the derivative evaluated at a specific input $x=10$."
"It is important to reiterate that MyGrad *never gives us the actual function* $\\frac{\\mathrm{d}f}{\\mathrm{d}x}$; it only computes the derivative evaluated at a specific input $x=5$."
]
},
{
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