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docs/contents/frameworks/frameworks.html

@@ -1427,7 +1427,7 @@ <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. By analyzing these three aspects—models, hardware, and software—ML engineers can select the optimal framework and customize it as needed for efficient and performant on-device ML applications. The goal is to balance model complexity, hardware limitations, and software integration to design a tailored ML pipeline for embedded and edge devices.</p>
-<div id="fig-tf-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - General">
+<div id="fig-tf-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-caption="TensorFlow Framework Comparison - General" data-align="center">
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 </section>
 <section id="software" class="level3" data-number="6.9.2">
 <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="header-section-number">6.9.2</span> Software</h3>
-<div id="fig-tf-sw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Model">
+<div id="fig-tf-sw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-caption="TensorFlow Framework Comparison - Model" data-align="center">
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@@ -1457,7 +1457,7 @@ <h3 data-number="6.9.2" class="anchored" data-anchor-id="software"><span class="
 </section>
 <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>
-<div id="fig-tf-hw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware">
+<div id="fig-tf-hw-comparison" class="quarto-figure quarto-figure-center quarto-float anchored" data-caption="TensorFlow Framework Comparison - Hardware" data-align="center">
 <figure class="quarto-float quarto-float-fig figure">
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 <img src="images/png/image3.png" style="width:100.0%" data-align="center" data-caption="TensorFlow Framework Comparison - Hardware" class="figure-img">
@@ -1504,7 +1504,7 @@ <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>6.10</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-figure quarto-figure-center quarto-float anchored" data-align="center" data-caption="Four Abstractions in Current ML System Stack">
+<div id="fig-mlsys-stack" class="quarto-figure quarto-figure-center quarto-float 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">
 <img src="images/png/image8.png" class="img-fluid figure-img" data-align="center" data-caption="Four Abstractions in Current ML System Stack">