From deaae60a49d0e1177e16a6f364502807e1f3547c Mon Sep 17 00:00:00 2001
From: NeroBlackstone <NeroBlackstone@users.noreply.github.com>
Date: Mon, 28 Oct 2024 05:44:39 +0000
Subject: [PATCH] deploy: 7bb4a632a31ff04dcdc388ffa89f416f23cd9bf0

---
 .../Concise Implementation of Linear Regression.ipynb         | 4 ++--
 .../Concise Implementation of Linear Regression.html          | 4 ++--
 searchindex.js                                                | 2 +-
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/_sources/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.ipynb b/_sources/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.ipynb
index c048797..080a5ef 100644
--- a/_sources/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.ipynb	
+++ b/_sources/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.ipynb	
@@ -147,7 +147,7 @@
     "\n",
     "For standard operations, we can use a framework’s predefined layers, which allow us to focus on the layers used to construct the model rather than worrying about their implementation. Recall the architecture of a single-layer network as described in Fig. 3.1.2. The layer is called fully connected, since each of its inputs is connected to each of its outputs by means of a matrix-vector multiplication.\n",
     "\n",
-    "In Flux, a `Dense(2 => 1)` layer denotes a layer of one neuron with two inputs (two feature) and one output. "
+    "In Lux, a `Dense(2 => 1)` layer denotes a layer of one neuron with two inputs (two feature) and one output. "
    ]
   },
   {
@@ -187,7 +187,7 @@
    "id": "c717e1ec-be46-4e88-b238-5a3b3dbc745a",
    "metadata": {},
    "source": [
-    "The `Flux.mse` function computes the mean squared error. By default, `Flux.mse` returns the average loss over examples. It is faster (and easier to use) than implementing our own."
+    "The `MSELoss()` function computes the mean squared error. By default, `MSELoss()` returns the average loss over examples. It is faster (and easier to use) than implementing our own."
    ]
   },
   {
diff --git a/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.html b/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.html
index 82dd09e..e22aed6 100644
--- a/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.html	
+++ b/notebooks/chapter_linear_regression/Concise Implementation of Linear Regression.html	
@@ -495,7 +495,7 @@ <h2><span class="section-number">8.2. </span>Reading the Dataset<a class="header
 <section id="defining-the-model">
 <h2><span class="section-number">8.3. </span>Defining the Model<a class="headerlink" href="#defining-the-model" title="Link to this heading">#</a></h2>
 <p>For standard operations, we can use a framework’s predefined layers, which allow us to focus on the layers used to construct the model rather than worrying about their implementation. Recall the architecture of a single-layer network as described in Fig. 3.1.2. The layer is called fully connected, since each of its inputs is connected to each of its outputs by means of a matrix-vector multiplication.</p>
-<p>In Flux, a <code class="docutils literal notranslate"><span class="pre">Dense(2</span> <span class="pre">=&gt;</span> <span class="pre">1)</span></code> layer denotes a layer of one neuron with two inputs (two feature) and one output.</p>
+<p>In Lux, a <code class="docutils literal notranslate"><span class="pre">Dense(2</span> <span class="pre">=&gt;</span> <span class="pre">1)</span></code> layer denotes a layer of one neuron with two inputs (two feature) and one output.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-julia notranslate"><div class="highlight"><pre><span></span><span class="k">using</span><span class="w"> </span><span class="n">Lux</span><span class="p">,</span><span class="n">Random</span>
@@ -514,7 +514,7 @@ <h2><span class="section-number">8.3. </span>Defining the Model<a class="headerl
 </section>
 <section id="defining-the-loss-function">
 <h2><span class="section-number">8.4. </span>Defining the Loss Function<a class="headerlink" href="#defining-the-loss-function" title="Link to this heading">#</a></h2>
-<p>The <code class="docutils literal notranslate"><span class="pre">Flux.mse</span></code> function computes the mean squared error. By default, <code class="docutils literal notranslate"><span class="pre">Flux.mse</span></code> returns the average loss over examples. It is faster (and easier to use) than implementing our own.</p>
+<p>The <code class="docutils literal notranslate"><span class="pre">MSELoss()</span></code> function computes the mean squared error. By default, <code class="docutils literal notranslate"><span class="pre">MSELoss()</span></code> returns the average loss over examples. It is faster (and easier to use) than implementing our own.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-julia notranslate"><div class="highlight"><pre><span></span><span class="k">const</span><span class="w"> </span><span class="n">mse</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">MSELoss</span><span class="p">()</span>
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"while": [5, 10, 11, 13, 14, 15, 16, 18, 23], "white": 5, "whole": [7, 15], "whop": 5, "whose": [13, 15, 16, 23], "why": [7, 11], "wide": 5, "widespread": 6, "width": 5, "wipe": 13, "wisdom": 10, "wish": [7, 13, 15, 17, 18], "wit": 6, "without": [7, 15], "word": 23, "work": [1, 5, 6, 13, 15, 23], "workload": 7, "worri": 6, "would": [1, 5, 10, 18, 23], "wrangl": 10, "write": [0, 2, 7, 15, 16, 19, 20, 21, 23], "written": 23, "wsampl": [19, 20, 21, 23], "wz6d4": [19, 20, 21, 23], "x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "x2": [2, 15], "xiao": 5, "xlabel": [4, 7, 8, 9, 10, 14, 18, 20, 21, 22, 23], "xoshiro": 6, "xscale": 23, "xtick": [7, 18, 22], "xticklabelsvis": 5, "xticksvis": 5, "y": [2, 4, 5, 6, 8, 9, 13, 15, 16, 17, 19, 20, 21, 22, 23], "y2": 2, "y_clone": 2, "year": [7, 18], "yield": [7, 13], "ylabel": [7, 10, 14, 18, 20, 21, 22, 23], "yml": 0, "you": [0, 5, 6, 9, 10, 11, 12, 13, 15, 16, 17, 18], "your": [0, 11, 12, 16], "yscale": 23, "ytick": [18, 22], "yticklabelsvis": 5, "yticksvis": 5, "z": [13, 19, 20, 21, 23], "za": [19, 20, 21, 23], "zero": [2, 10, 11, 15], "zip": [5, 7, 19, 20, 21, 23], "zipfian": 23, "zone": 11, "zygot": [6, 10, 13], "\u03bc": 7, "\u03c3": 7}, "titles": ["Dive into Deep Learning - Julia", "<span class=\"section-number\">15. </span>Parameter Management", "<span class=\"section-number\">16. </span>File I/O", "<span class=\"section-number\">17. </span>GPUs", "<span class=\"section-number\">10. </span>Concise Implementation of Softmax Regression", "<span class=\"section-number\">9. </span>The Image Classification Dataset", "<span class=\"section-number\">8. </span>Concise Implementation of Linear Regression", "<span class=\"section-number\">7. </span>Linear Regression", "<span class=\"section-number\">14. </span>Dropout", "<span class=\"section-number\">12. </span>Implementation of Multilayer Perceptrons", "<span class=\"section-number\">11. </span>Multilayer Perceptrons", "<span class=\"section-number\">13. </span>Numerical Stability and Initialization", "Predicting House Prices on Kaggle", "<span class=\"section-number\">5. </span>Automatic Differentiation", "<span class=\"section-number\">4. </span>Calculus", "<span class=\"section-number\">1. </span>Data Manipulation", "<span class=\"section-number\">2. </span>Data Preprocessing", "<span class=\"section-number\">3. </span>Linear Algebra", "<span class=\"section-number\">6. </span>Probability and Statistics", "<span class=\"section-number\">22. </span>Deep Recurrent Neural Networks", "<span class=\"section-number\">21. </span>Gated Recurrent Units (GRU)", "<span class=\"section-number\">20. </span>Long Short-Term Memory (LSTM)", "<span class=\"section-number\">18. </span>Working with Sequences", "<span class=\"section-number\">19. </span>Concise Implementation of Recurrent Neural Networks"], "titleterms": {"A": [13, 18], "One": 23, "The": [5, 7], "access": 1, "activ": 10, "after": 0, "algebra": 17, 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\ No newline at end of file