Small changes to intro.md

_build/html/_sources/intro.md

@@ -1,6 +1,6 @@
 # {ml-nn-zero2hero}
 
-Welcome to **Collection of notes on {ml.nn-zero2hero}**. This repository includes notes and learnings following the Youtube Playlist on [nn_zero_to_hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) guide by Andrej Karpathy and the open-source notebooks available from the book on [Hands-On Machine Learning and Deep Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/) 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.
+Welcome to **Collection of notes on {ml.nn-zero2hero}**. This repository includes notes and learnings following the Youtube Playlist on [nn_zero_to_hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ) guide by Andrej Karpathy and the open-source notebooks available from the book on [Hands-On Machine Learning and Deep Learning](https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/) 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.
 
 Other resources include (but not limited to),
 - The [DeepLearning.ai](https://deeplearning.ai/) courses on the Machine Learning / Deep Learning Specialization by Andrew Ng
@@ -16,19 +16,20 @@ The goals of this collection of notebooks are to:
 * 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)
-- Scientific Python libraries, in particular NumPy, matplotlib and pandas.
-- Mathematical notions of Linear Algebra, Calculus, Statistics and Probability theory.
+- Python 3.9 or above (preferably on VSCode)
+- Scientific Python libraries, in particular NumPy, matplotlib and pandas
+- Mathematical notions of Linear Algebra, Calculus, Statistics and Probability theory
     - [Mathematics for Machine Learning](https://mml-book.github.io/book/mml-book.pdf) Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong
-- Jupyter – These notebooks are based on Jupyter. You can run these notebooks in just one click using Google Colaboratory.
+- Jupyter – These notebooks are based on Jupyter. You can run these notebooks in just one click using Google Colaboratory
 
 #### Packages
 - numpy
-- scikit-learn
 - matplotlib
 - pandas
+- scikit-learn
 - tensorflow2
 - keras
 

_build/html/intro.html

@@ -445,7 +445,7 @@ <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 <strong>Collection of notes on {ml.nn-zero2hero}</strong>. This repository includes notes and learnings following the Youtube Playlist on <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 from the book on <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/">Hands-On Machine Learning and Deep Learning</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>Welcome to <strong>Collection of notes on {ml.nn-zero2hero}</strong>. This repository includes notes and learnings following the Youtube Playlist on <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 from the book on <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781098125967/">Hands-On Machine Learning and Deep Learning</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.</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>
@@ -466,22 +466,22 @@ <h1>{ml-nn-zero2hero}<a class="headerlink" href="#ml-nn-zero2hero" title="Link t
 <section id="pre-requisites">
 <h2>Pre-requisites<a class="headerlink" href="#pre-requisites" title="Link to this heading">#</a></h2>
 <ul class="simple">
-<li><p>Python 3.9 or above (preferrably on VSCode)</p></li>
-<li><p>Scientific Python libraries, in particular NumPy, matplotlib and pandas.</p></li>
-<li><p>Mathematical notions of Linear Algebra, Calculus, Statistics and Probability theory.</p>
+<li><p>Python 3.9 or above (preferably on VSCode)</p></li>
+<li><p>Scientific Python libraries, in particular NumPy, matplotlib and pandas</p></li>
+<li><p>Mathematical notions of Linear Algebra, Calculus, Statistics and Probability theory</p>
 <ul>
 <li><p><a class="reference external" href="https://mml-book.github.io/book/mml-book.pdf">Mathematics for Machine Learning</a> Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong</p></li>
 </ul>
 </li>
-<li><p>Jupyter – These notebooks are based on Jupyter. You can run these notebooks in just one click using Google Colaboratory.</p></li>
+<li><p>Jupyter – These notebooks are based on Jupyter. You can run these notebooks in just one click using Google Colaboratory</p></li>
 </ul>
 <section id="packages">
 <h3>Packages<a class="headerlink" href="#packages" title="Link to this heading">#</a></h3>
 <ul class="simple">
 <li><p>numpy</p></li>
-<li><p>scikit-learn</p></li>
 <li><p>matplotlib</p></li>
 <li><p>pandas</p></li>
+<li><p>scikit-learn</p></li>
 <li><p>tensorflow2</p></li>
 <li><p>keras</p></li>
 </ul>