Push dev branch build

docs/contents/ai/socratiq.html

@@ -674,9 +674,9 @@ <h2 class="anchored" data-anchor-id="quick-start-guide">Quick Start Guide</h2>
 <h2 class="anchored" data-anchor-id="button-overview">Button Overview</h2>
 <p>The top nav bar provices quick access to the following features:</p>
 <ol type="1">
-<li>Adjust your <a href="@sec-socratiq-settings">settings</a> at any time.</li>
-<li>Track your <a href="@sec-socratiq-dashboard">progress</a> by viewing the dashboard.</li>
-<li>Start new or save your <a href="@sec-socratiq-learning}">conversations</a> with SocratiQ.</li>
+<li>Adjust your <a href="#sec-socratiq-settings">settings</a> at any time.</li>
+<li>Track your <a href="#sec-socratiq-dashboard">progress</a> by viewing the dashboard.</li>
+<li>Start new or save your <a href="#sec-socratiq-learning">conversations</a> with SocratiQ.</li>
 </ol>
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docs/contents/core/frameworks/frameworks.html

@@ -1610,7 +1610,7 @@ <h3 data-number="6.8.3" class="anchored" data-anchor-id="library"><span class="h
 <section id="choosing-the-right-framework" class="level2" data-number="6.9">
 <h2 data-number="6.9" class="anchored" data-anchor-id="choosing-the-right-framework"><span class="header-section-number">6.9</span> Choosing the Right Framework</h2>
 <p>Choosing the right machine learning framework for a given application requires carefully evaluating models, hardware, and software considerations. <a href="#fig-tf-comparison" class="quarto-xref">Figure&nbsp;<span>6.13</span></a> provides a comparison of different TensorFlow frameworks, which we’ll discuss in more detail:</p>
-<div id="fig-tf-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - General">
+<div id="fig-tf-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - General" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-tf-comparison-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="images/png/image4.png" class="lightbox" data-gallery="quarto-lightbox-gallery-14" title="Figure&nbsp;6.13: TensorFlow Framework Comparison - General. Source: TensorFlow."><img src="images/png/image4.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - General" class="figure-img"></a>
@@ -1630,7 +1630,7 @@ <h3 data-number="6.9.1" class="anchored" data-anchor-id="model"><span class="hea
 <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="header-section-number">6.9.2</span> Software</h3>
 <p>As shown in <a href="#fig-tf-sw-comparison" class="quarto-xref">Figure&nbsp;<span>6.14</span></a>, TensorFlow Lite Micro does not have OS support, while TensorFlow and TensorFlow Lite do. This design choice for TensorFlow Lite Micro helps reduce memory overhead, make startup times faster, and consume less energy. Instead, TensorFlow Lite Micro can be used in conjunction with real-time operating systems (RTOS) like FreeRTOS, Zephyr, and Mbed OS.</p>
 <p>The figure also highlights an important memory management feature: TensorFlow Lite and TensorFlow Lite Micro support model memory mapping, allowing models to be directly accessed from flash storage rather than loaded into RAM. In contrast, TensorFlow does not offer this capability.</p>
-<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Model">
+<div id="fig-tf-sw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Model" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-tf-sw-comparison-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="images/png/image5.png" class="lightbox" data-gallery="quarto-lightbox-gallery-15" title="Figure&nbsp;6.14: TensorFlow Framework Comparison - Software. Source: TensorFlow."><img src="images/png/image5.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - Model" class="figure-img"></a>
@@ -1646,7 +1646,7 @@ <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="
 <section id="hardware" class="level3" data-number="6.9.3">
 <h3 data-number="6.9.3" class="anchored" data-anchor-id="hardware"><span class="header-section-number">6.9.3</span> Hardware</h3>
 <p>TensorFlow Lite and TensorFlow Lite Micro have significantly smaller base binary sizes and memory footprints than TensorFlow (see <a href="#fig-tf-hw-comparison" class="quarto-xref">Figure&nbsp;<span>6.15</span></a>). For example, a typical TensorFlow Lite Micro binary is less than 200KB, whereas TensorFlow is much larger. This is due to the resource-constrained environments of embedded systems. TensorFlow supports x86, TPUs, and GPUs like NVIDIA, AMD, and Intel.</p>
-<div id="fig-tf-hw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware">
+<div id="fig-tf-hw-comparison" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="TensorFlow Framework Comparison - Hardware" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-tf-hw-comparison-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="images/png/image3.png" class="lightbox" data-gallery="quarto-lightbox-gallery-16" title="Figure&nbsp;6.15: TensorFlow Framework Comparison - Hardware. Source: TensorFlow."><img src="images/png/image3.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware" class="figure-img"></a>
@@ -1693,7 +1693,7 @@ <h2 data-number="6.10" class="anchored" data-anchor-id="future-trends-in-ml-fram
 <section id="decomposition" class="level3" data-number="6.10.1">
 <h3 data-number="6.10.1" class="anchored" data-anchor-id="decomposition"><span class="header-section-number">6.10.1</span> Decomposition</h3>
 <p>Currently, the ML system stack consists of four abstractions as shown in <a href="#fig-mlsys-stack" class="quarto-xref">Figure&nbsp;<span>6.16</span></a>, namely (1) computational graphs, (2) tensor programs, (3) libraries and runtimes, and (4) hardware primitives.</p>
-<div id="fig-mlsys-stack" class="quarto-float quarto-figure quarto-figure-center anchored" data-align="center" data-caption="Four Abstractions in Current ML System Stack">
+<div id="fig-mlsys-stack" class="quarto-float quarto-figure quarto-figure-center anchored" data-caption="Four Abstractions in Current ML System Stack" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
 <div aria-describedby="fig-mlsys-stack-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
 <a href="images/png/image8.png" class="lightbox" data-gallery="quarto-lightbox-gallery-17" title="Figure&nbsp;6.16: Four abstractions in current ML system stacks. Source: TVM."><img src="images/png/image8.png" class="img-fluid figure-img" data-align="center" data-caption="Four Abstractions in Current ML System Stack"></a>