re-commit

_build/html/_sources/intro.md

@@ -8,7 +8,7 @@
 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
+- The [DeepLearning.ai](https://deeplearning.ai/) courses for Machine Learning and Deep Learning Specializations by Andrew Ng
 - The [MIT Deep Learning book](https://www.deeplearningbook.org/) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
 - Online book on [Model-based Machine Learning](https://mbmlbook.com/) by John Winn
 - Nvidia's [Learning Deep Learning](https://www.nvidia.com/en-us/training/books/) by Magnus Ekman
@@ -42,7 +42,4 @@ I want to publish my work at [NuerIPS 2025](https://nips.cc/) :)
 
 
 Get started
-```
-
-
-https://nips.cc/Conferences/2024/CallForPapers
+```

_build/html/intro.html

@@ -450,7 +450,7 @@ <h1>{ml-nn-zero2hero}<a class="headerlink" href="#ml-nn-zero2hero" title="Link t
 <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>
+<li><p>The <a class="reference external" href="https://deeplearning.ai/">DeepLearning.ai</a> courses for Machine Learning and Deep Learning Specializations by Andrew Ng</p></li>
 <li><p>The <a class="reference external" href="https://www.deeplearningbook.org/">MIT Deep Learning book</a> by Ian Goodfellow and Yoshua Bengio and Aaron Courville</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>Nvidia’s <a class="reference external" href="https://www.nvidia.com/en-us/training/books/">Learning Deep Learning</a> by Magnus Ekman</p></li>
@@ -485,7 +485,6 @@ <h2>Pre-requisites<a class="headerlink" href="#pre-requisites" title="Link to th
 </ul>
 <p><a class="sd-sphinx-override sd-btn sd-text-wrap sd-btn-primary sd-rounded-pill float-left reference internal" href="notebooks/00/00.html"><span class="doc">
 Get started</span></a></p>
-<p><a class="reference external" href="https://nips.cc/Conferences/2024/CallForPapers">https://nips.cc/Conferences/2024/CallForPapers</a></p>
 </section>
 <div class="toctree-wrapper compound">
 </div>