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diff --git a/README.md b/README.md
@@ -2,4 +2,4 @@
 
 ### Disclaimer: All the rights of these codes and resources belong to their original authors. I am compiling a collection for self-learning purposes and to build tools by myself using the knowledge.
 
-I learned how to build these notes like this thanks to the source repository of [Hands-On Mathematical Optimization with Python](https://github.com/mobook/MO-book) and the [Jupyter Book](https://jupyterbook.org/en/stable/start/build.html) page on how to build beautiful, publication-quality books and documents from computational content.
+I learned how to build these notes like as Jupyter-book thanks to this source repository on [Hands-On Mathematical Optimization with Python](https://github.com/mobook/MO-book) and the [Jupyter Book](https://jupyterbook.org/en/stable/start/build.html) official page on how to build beautiful, publication-quality books and documents from computational content.
diff --git a/_build/.doctrees/environment.pickle b/_build/.doctrees/environment.pickle
diff --git a/_build/.doctrees/intro.doctree b/_build/.doctrees/intro.doctree
diff --git a/_build/.doctrees/notebooks/01/01.doctree b/_build/.doctrees/notebooks/01/01.doctree
diff --git a/_build/html/_sources/intro.md b/_build/html/_sources/intro.md
@@ -1,30 +1,33 @@
 # {ml-nn-zero2hero}
 
-Welcome to this repository of Collection of notes on {ml.nn-zero2hero}. This repository includes notes and learnings following the [nn_zero_to_hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) guide by Andrej Karpathy and the open-source notebooks available on [Hands-On Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2](https://github.com/ageron/handson-ml3) by Aurelien Geron. Personally, I found these to be two of the best resources available on this subject, purely from an applied learning stand point. For great visual understanding, the videos at [3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) are quite helpful as well. I should also look into getting a concise theoretical background on the subject, and will post a good article on this as well.
+Welcome to **Collection of notes on {ml.nn-zero2hero}**. This repository includes notes and learnings following the [nn_zero_to_hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) guide by Andrej Karpathy and the open-source notebooks available on [Hands-On Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2](https://github.com/ageron/handson-ml3) by Aurelien Geron. Personally, I found these to be two of the best resources available on this subject, purely from an applied learning stand point. For great visual understanding, the videos on the Youtube Channel [3Blue1Brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi) are quite helpful as well. I should also look into getting a concise theoretical background on the subject, and will post a good article on this as well.
 
 Other resources include (but not limited to),
 - The [DeepLearning.ai](https://deeplearning.ai/) courses on the Machine Learning / Deep Learning Specialization by Andrew Ng
 - Online book on [Model-based Machine Learning](https://mbmlbook.com/) by John Winn
 - [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) by Christopher Bishop
 - [Hands-On Mathematical Optimization with Python](https://github.com/mobook/MO-book)
 
+```{note}
 The goals of this collection of notebooks are to:
 
 * Grasp the foundations for hands-on machine learning and deep learning in an elegant approach
 * Have fun while learning and compiling notes and eventually end in a culmination of work
 * Help readers to easily navigate the learning experience with a good compilation on notes on the subject
 * Demonstrate the use of SOTA tools and concepts for documenting beautiful publication-quality work
-
+```
 ### Pre-requisites
 
 - Python 3.9 or above (preferrably on VSCode)
 - [Mathematics for Machine Learning](https://mml-book.github.io/book/mml-book.pdf)
 
 #### Packages
-
 - numpy
 - scikit-learn
 - matplotlib
 - pandas
 - tensorflow2
 - keras
+
+```{tableofcontents}
+```
diff --git a/_build/html/genindex.html b/_build/html/genindex.html
@@ -179,7 +179,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/intro.html b/_build/html/intro.html
@@ -181,7 +181,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">
@@ -420,7 +419,9 @@ <h2> Contents </h2>
             <nav aria-label="Page">
                 <ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pre-requisites">Pre-requisites</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#packages">Packages</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#packages">Packages</a><ul class="nav section-nav flex-column">
+</ul>
+</li>
 </ul>
 </li>
 </ul>
@@ -436,21 +437,24 @@ <h2> Contents </h2>
 
   <section class="tex2jax_ignore mathjax_ignore" id="ml-nn-zero2hero">
 <h1>{ml-nn-zero2hero}<a class="headerlink" href="#ml-nn-zero2hero" title="Link to this heading">#</a></h1>
-<p>Welcome to this repository of Collection of notes on {ml.nn-zero2hero}. This repository includes notes and learnings following the <a class="reference external" href="https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ">nn_zero_to_hero</a> guide by Andrej Karpathy and the open-source notebooks available on <a class="reference external" href="https://github.com/ageron/handson-ml3">Hands-On Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2</a> by Aurelien Geron. Personally, I found these to be two of the best resources available on this subject, purely from an applied learning stand point. For great visual understanding, the videos at <a class="reference external" href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi">3Blue1Brown</a> are quite helpful as well. I should also look into getting a concise theoretical background on the subject, and will post a good article on this as well.</p>
+<p>Welcome to <strong>Collection of notes on {ml.nn-zero2hero}</strong>. This repository includes notes and learnings following the <a class="reference external" href="https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ">nn_zero_to_hero</a> guide by Andrej Karpathy and the open-source notebooks available on <a class="reference external" href="https://github.com/ageron/handson-ml3">Hands-On Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2</a> by Aurelien Geron. Personally, I found these to be two of the best resources available on this subject, purely from an applied learning stand point. For great visual understanding, the videos on the Youtube Channel <a class="reference external" href="https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi">3Blue1Brown</a> are quite helpful as well. I should also look into getting a concise theoretical background on the subject, and will post a good article on this as well.</p>
 <p>Other resources include (but not limited to),</p>
 <ul class="simple">
 <li><p>The <a class="reference external" href="https://deeplearning.ai/">DeepLearning.ai</a> courses on the Machine Learning / Deep Learning Specialization by Andrew Ng</p></li>
 <li><p>Online book on <a class="reference external" href="https://mbmlbook.com/">Model-based Machine Learning</a> by John Winn</p></li>
 <li><p><a class="reference external" href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf">Pattern Recognition and Machine Learning</a> by Christopher Bishop</p></li>
 <li><p><a class="reference external" href="https://github.com/mobook/MO-book">Hands-On Mathematical Optimization with Python</a></p></li>
 </ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
 <p>The goals of this collection of notebooks are to:</p>
 <ul class="simple">
 <li><p>Grasp the foundations for hands-on machine learning and deep learning in an elegant approach</p></li>
 <li><p>Have fun while learning and compiling notes and eventually end in a culmination of work</p></li>
 <li><p>Help readers to easily navigate the learning experience with a good compilation on notes on the subject</p></li>
 <li><p>Demonstrate the use of SOTA tools and concepts for documenting beautiful publication-quality work</p></li>
 </ul>
+</div>
 <section id="pre-requisites">
 <h2>Pre-requisites<a class="headerlink" href="#pre-requisites" title="Link to this heading">#</a></h2>
 <ul class="simple">
@@ -467,18 +471,44 @@ <h3>Packages<a class="headerlink" href="#packages" title="Link to this heading">
 <li><p>tensorflow2</p></li>
 <li><p>keras</p></li>
 </ul>
-</section>
-</section>
 <div class="toctree-wrapper compound">
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Machine Learning</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/01/01.html">Section 01 - Exploring for Machine Learning</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/02/02.html">Section 02</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/03/03.html">Section 03</a></li>
+</ul>
 </div>
 <div class="toctree-wrapper compound">
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deep Learning</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/04/04.html">Section 04</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/05/05.html">Section 05</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/06/06.html">Section 06</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/07/07.html">Section 07</a></li>
+</ul>
 </div>
 <div class="toctree-wrapper compound">
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Transformers</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/08/08.html">Section 08</a></li>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/09/09.html">Section 09</a></li>
+</ul>
 </div>
 <div class="toctree-wrapper compound">
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Optimization</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="notebooks/10/10.html">Section 10</a></li>
+</ul>
 </div>
 <div class="toctree-wrapper compound">
+<p aria-level="2" class="caption" role="heading"><span class="caption-text">Other Notes</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
+</ul>
 </div>
+</section>
+</section>
 </section>
 
     <script type="text/x-thebe-config">
@@ -537,7 +567,9 @@ <h3>Packages<a class="headerlink" href="#packages" title="Link to this heading">
   <nav class="bd-toc-nav page-toc">
     <ul class="visible nav section-nav flex-column">
 <li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#pre-requisites">Pre-requisites</a><ul class="nav section-nav flex-column">
-<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#packages">Packages</a></li>
+<li class="toc-h3 nav-item toc-entry"><a class="reference internal nav-link" href="#packages">Packages</a><ul class="nav section-nav flex-column">
+</ul>
+</li>
 </ul>
 </li>
 </ul>

diff --git a/_build/html/notebooks/01/01.html b/_build/html/notebooks/01/01.html
@@ -180,7 +180,6 @@
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">Section 01 - Exploring for Machine Learning</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/02/02.html b/_build/html/notebooks/02/02.html
@@ -180,7 +180,6 @@
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">Section 02</a><input checked="" class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/03/03.html b/_build/html/notebooks/03/03.html
@@ -180,7 +180,6 @@
 <ul class="current nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/04/04.html b/_build/html/notebooks/04/04.html
@@ -180,7 +180,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/05/05.html b/_build/html/notebooks/05/05.html
@@ -180,7 +180,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/06/06.html b/_build/html/notebooks/06/06.html
@@ -180,7 +180,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">

diff --git a/_build/html/notebooks/07/07.html b/_build/html/notebooks/07/07.html
@@ -180,7 +180,6 @@
 <ul class="nav bd-sidenav">
 <li class="toctree-l1 has-children"><a class="reference internal" href="../01/01.html">Section 01 - Exploring for Machine Learning</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-1"><i class="fa-solid fa-chevron-down"></i></label><ul>
 <li class="toctree-l2"><a class="reference internal" href="../01/NB_1_1.html">Section 1.1</a></li>
-<li class="toctree-l2"><a class="reference external" href="https://jupyterbook.org/en/stable/start/your-first-book.html">jupyter-books</a></li>
 </ul>
 </li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="../02/02.html">Section 02</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" type="checkbox"/><label class="toctree-toggle" for="toctree-checkbox-2"><i class="fa-solid fa-chevron-down"></i></label><ul class="simple">
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2,4 +2,4 @@

		### Disclaimer: All the rights of these codes and resources belong to their original authors. I am compiling a collection for self-learning purposes and to build tools by myself using the knowledge.

		I learned how to build these notes like this thanks to the source repository of [Hands-On Mathematical Optimization with Python](https://github.com/mobook/MO-book) and the [Jupyter Book](https://jupyterbook.org/en/stable/start/build.html) page on how to build beautiful, publication-quality books and documents from computational content.
		I learned how to build these notes like as Jupyter-book thanks to this source repository on [Hands-On Mathematical Optimization with Python](https://github.com/mobook/MO-book) and the [Jupyter Book](https://jupyterbook.org/en/stable/start/build.html) official page on how to build beautiful, publication-quality books and documents from computational content.