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diff --git a/02-image-data.html b/02-image-data.html
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@@ -547,7 +547,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Python image libraries </h3>
 </button>
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 <p>Two of the most commonly used libraries for image representation and
 manipulation are NumPy and Pillow (PIL). Additionally, when working with
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Why normalise? </h3>
 </button>
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 <p>ChatGPT</p>
 <p>Normalizing the RGB values to be between 0 and 1 is a common
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 <p>A. Training Set</p>
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Data Splitting Techniques </h3>
 </button>
-<div id="collapseSpoiler3" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler3" aria-labelledby="headingSpoiler3">
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 <p>ChatGPT</p>
 <p>Data is typically split into the training, validation, and test data
diff --git a/03-build-cnn.html b/03-build-cnn.html
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> What does Flatten mean exactly? </h3>
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 <p>A flatten layer function is typically used to transform the
 two-dimensional arrays (matrices) generated by the convolutional and
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Explain the summary output </h3>
 </button>
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 <p>The Model.summary() output has three columns:</p>
 <ol style="list-style-type: decimal"><li>Layer (type) lists the name and type of each layer.  
diff --git a/04-fit-cnn.html b/04-fit-cnn.html
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Learning Rate </h3>
 </button>
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 <p>ChatGPT</p>
 <p><strong>Learning rate</strong> is a hyperparameter that determines
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Batch size </h3>
 </button>
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 <p>ChatGPT</p>
 <p>The choice of batch size can have various implications, and there are
@@ -822,7 +822,7 @@ <h3 class="callout-title">CHALLENGE Inspect the Training Curve<a class="anchor"
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 </button>
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 <ol style="list-style-type: decimal"><li>Key things to look for for:  
 <ul><li>Loss
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: What is underfitting? </h3>
 </button>
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 <p>Underfitting occurs when the model is too simple or lacks the
 capacity to capture the underlying patterns and relationships present in
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   <h3 class="accordion-header" id="headingSpoiler4">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Regularization techniques </h3>
 </button>
-<div id="collapseSpoiler4" class="accordion-collapse collapse" aria-labelledby="headingSpoiler4" data-bs-parent="#accordionSpoiler4">
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 <p>ChatGPT</p>
 <p>Regularization methods introduce constraints or penalties to the
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@@ -423,7 +423,7 @@ <h3 class="callout-title">CHALLENGE How big should our test data set be?<a class
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   <h4 class="accordion-header" id="headingSolution1"> Show me the solution </h4>
 </button>
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 <div class="accordion-body">
 <p>It depends! Recall in an <a href="02-image-data.html">Episode 02
 Introduction to Image Data</a> Callout we talked about the different
@@ -480,7 +480,7 @@ <h3 class="code-label">PYTHON<i aria-hidden="true" data-feather="chevron-left"><
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 </button>
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@@ -651,7 +651,7 @@ <h3 class="callout-title">CHALLENGE Confusion Matrix<a class="anchor" aria-label
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 <p>A1. The confusion matrix illustrates that the predictions are not bad
 but can be improved.</p>
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 <p>A1. Varying the dropout rate</p>
 <p>The code below instantiates and trains a model with varying dropout
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 <p>The code below serves as a practical means for exploring activation
 performance on an image dataset.</p>
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 <p>This is an open question.</p>
 <p>Regarding the model architecture:</p>
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Python image libraries </h3>
 </button>
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 <p>Two of the most commonly used libraries for image representation and
 manipulation are NumPy and Pillow (PIL). Additionally, when working with
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Why normalise? </h3>
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 <p>ChatGPT</p>
 <p>Normalizing the RGB values to be between 0 and 1 is a common
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 <p>A. Training Set</p>
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Data Splitting Techniques </h3>
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 <p>ChatGPT</p>
 <p>Data is typically split into the training, validation, and test data
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> What does Flatten mean exactly? </h3>
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 <p>A flatten layer function is typically used to transform the
 two-dimensional arrays (matrices) generated by the convolutional and
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Explain the summary output </h3>
 </button>
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 <p>The Model.summary() output has three columns:</p>
 <ol style="list-style-type: decimal">
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Learning Rate </h3>
 </button>
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 <p>ChatGPT</p>
 <p><strong>Learning rate</strong> is a hyperparameter that determines
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Batch size </h3>
 </button>
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 <p>ChatGPT</p>
 <p>The choice of batch size can have various implications, and there are
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 <ol style="list-style-type: decimal">
 <li>Key things to look for for:  
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: What is underfitting? </h3>
 </button>
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 <p>Underfitting occurs when the model is too simple or lacks the
 capacity to capture the underlying patterns and relationships present in
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 <h3 class="code-label">PYTHON<i aria-hidden="true" data-feather="chevron-left"></i><i aria-hidden="true" data-feather="chevron-right"></i>
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 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler4" aria-expanded="false" aria-controls="collapseSpoiler4">
   <h3 class="accordion-header" id="headingSpoiler4">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Regularization techniques </h3>
 </button>
-<div id="collapseSpoiler4" class="accordion-collapse collapse" aria-labelledby="headingSpoiler4" data-bs-parent="#accordionSpoiler4">
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 <p>ChatGPT</p>
 <p>Regularization methods introduce constraints or penalties to the
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 <p>A1. The confusion matrix illustrates that the predictions are not bad
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 <p>The code below serves as a practical means for exploring activation
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 <p>This is an open question.</p>
 <p>Regarding the model architecture:</p>
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Python image libraries </h3>
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 <p>Two of the most commonly used libraries for image representation and
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Why normalise? </h3>
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 <p>ChatGPT</p>
 <p>Normalizing the RGB values to be between 0 and 1 is a common
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Data Splitting Techniques </h3>
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 <p>ChatGPT</p>
 <p>Data is typically split into the training, validation, and test data
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 <p>A flatten layer function is typically used to transform the
 two-dimensional arrays (matrices) generated by the convolutional and
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 <p>The Model.summary() output has three columns:</p>
 <ol style="list-style-type: decimal"><li>Layer (type) lists the name and type of each layer.  
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Learning Rate </h3>
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 <p>ChatGPT</p>
 <p><strong>Learning rate</strong> is a hyperparameter that determines
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 <p>ChatGPT</p>
 <p>The choice of batch size can have various implications, and there are
@@ -824,7 +824,7 @@ <h3 class="callout-title">CHALLENGE Inspect the Training Curve<a class="anchor"
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 <div class="accordion-body">
 <ol style="list-style-type: decimal"><li>Key things to look for for:  
 <ul><li>Loss
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: What is underfitting? </h3>
 </button>
-<div id="collapseSpoiler3" class="accordion-collapse collapse" aria-labelledby="headingSpoiler3" data-bs-parent="#accordionSpoiler3">
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 <div class="accordion-body">
 <p>Underfitting occurs when the model is too simple or lacks the
 capacity to capture the underlying patterns and relationships present in
@@ -982,7 +982,7 @@ <h3 class="callout-title">CHALLENGE Create a function that defines a CNN with Dr
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 <div class="codewrapper sourceCode" id="cb14">
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@@ -1102,7 +1102,7 @@ <h3 class="code-label">PYTHON<i aria-hidden="true" data-feather="chevron-left"><
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler4" aria-expanded="false" aria-controls="collapseSpoiler4">
   <h3 class="accordion-header" id="headingSpoiler4">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Regularization techniques </h3>
 </button>
-<div id="collapseSpoiler4" class="accordion-collapse collapse" aria-labelledby="headingSpoiler4" data-bs-parent="#accordionSpoiler4">
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 <p>ChatGPT</p>
 <p>Regularization methods introduce constraints or penalties to the
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 <div class="accordion-body">
 <p>It depends! Recall in an <a href="02-image-data.html">Episode 02
 Introduction to Image Data</a> Callout we talked about the different
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@@ -653,7 +653,7 @@ <h3 class="callout-title">CHALLENGE Confusion Matrix<a class="anchor" aria-label
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 <div class="accordion-body">
 <p>A1. The confusion matrix illustrates that the predictions are not bad
 but can be improved.</p>
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 <p>A1. Varying the dropout rate</p>
 <p>The code below instantiates and trains a model with varying dropout
@@ -865,7 +865,7 @@ <h3 class="callout-title">CHALLENGE Tune Optimizer using Grid Search<a class="an
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 <p>The code below serves as a practical means for exploring activation
 performance on an image dataset.</p>
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 <p>This is an open question.</p>
 <p>Regarding the model architecture:</p>
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Python image libraries </h3>
 </button>
-<div id="collapseSpoiler1" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler1" aria-labelledby="headingSpoiler1">
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 <p>Two of the most commonly used libraries for image representation and
 manipulation are NumPy and Pillow (PIL). Additionally, when working with
@@ -1346,7 +1346,7 @@ <h3 class="code-label">OUTPUT<i aria-hidden="true" data-feather="chevron-left"><
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Why normalise? </h3>
 </button>
-<div id="collapseSpoiler2" class="accordion-collapse collapse" data-bs-parent="#accordionSpoiler2" aria-labelledby="headingSpoiler2">
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 <p>ChatGPT</p>
 <p>Normalizing the RGB values to be between 0 and 1 is a common
@@ -1605,7 +1605,7 @@ <h3 class="code-label">PYTHON<i aria-hidden="true" data-feather="chevron-left"><
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@@ -1706,7 +1706,7 @@ <h3 class="callout-title">CHALLENGE Training and Validation<a class="anchor" ari
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 <p>A. Training Set</p>
 <div class="codewrapper sourceCode" id="cb14">
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Data Splitting Techniques </h3>
 </button>
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 <p>ChatGPT</p>
 <p>Data is typically split into the training, validation, and test data
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   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> What does Flatten mean exactly? </h3>
 </button>
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 <p>A flatten layer function is typically used to transform the
 two-dimensional arrays (matrices) generated by the convolutional and
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 </button>
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> Explain the summary output </h3>
 </button>
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 <p>The Model.summary() output has three columns:</p>
 <ol style="list-style-type: decimal">
@@ -2963,7 +2963,7 @@ <h4 id="optimizer">Optimizer<a class="anchor" aria-label="anchor" href="#optimiz
 <button class="accordion-button spoiler-button collapsed" type="button" data-bs-toggle="collapse" data-bs-target="#collapseSpoiler1" aria-expanded="false" aria-controls="collapseSpoiler1">
   <h3 class="accordion-header" id="headingSpoiler1">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Learning Rate </h3>
 </button>
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 <p>ChatGPT</p>
 <p><strong>Learning rate</strong> is a hyperparameter that determines
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   <h3 class="accordion-header" id="headingSpoiler2">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Batch size </h3>
 </button>
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 <p>ChatGPT</p>
 <p>The choice of batch size can have various implications, and there are
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 <ol style="list-style-type: decimal">
 <li>Key things to look for for:  
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   <h3 class="accordion-header" id="headingSpoiler3">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: What is underfitting? </h3>
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 <p>Underfitting occurs when the model is too simple or lacks the
 capacity to capture the underlying patterns and relationships present in
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   <h3 class="accordion-header" id="headingSpoiler4">  <div class="note-square"><i aria-hidden="true" class="callout-icon" data-feather="eye"></i></div> WANT TO KNOW MORE: Regularization techniques </h3>
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 <p>ChatGPT</p>
 <p>Regularization methods introduce constraints or penalties to the
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 <div class="accordion-body">
 <p>It depends! Recall in an <a href="02-image-data.html">Episode 02
 Introduction to Image Data</a> Callout we talked about the different
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@@ -4070,7 +4070,7 @@ <h3 class="callout-title">CHALLENGE Confusion Matrix<a class="anchor" aria-label
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 <div class="accordion-body">
 <p>A1. The confusion matrix illustrates that the predictions are not bad
 but can be improved.</p>
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 <div class="accordion-body">
 <p>A1. Varying the dropout rate</p>
 <p>The code below instantiates and trains a model with varying dropout
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 <p>The code below serves as a practical means for exploring activation
 performance on an image dataset.</p>
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 <p>This is an open question.</p>
 <p>Regarding the model architecture:</p>
diff --git a/pkgdown.yml b/pkgdown.yml
index 06acf0c..47a2a96 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,5 +2,5 @@ pandoc: 3.1.11
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-06-25T02:04Z
+last_built: 2024-06-25T02:53Z