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(This article was first published on <strong><a href="https://gigadom.in/2018/12/14/my-book-deep-learning-from-first-principlessecond-edition-now-on-amazon/"> R – Giga thoughts …</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>The second edition of my book ‘Deep Learning from first principles:Second Edition- In vectorized Python, R and Octave’, is now available on Amazon, in both <a rel="nofollow" href="https://www.amazon.com/dp/1791596177" target="_blank">paperback</a> ($14.99)  and <a rel="nofollow" href="https://www.amazon.com/dp/B07LBG542L" target="_blank">kindle</a> ($9.99/Rs449/-)  versions. Since this book is almost 70% code, all functions, and code snippets have been formatted to use the fixed-width font ‘Lucida Console’. In addition line numbers have been added to all code snippets. This makes the code more organized and much more readable. I have also fixed typos in the book</p>
<p><img data-orig-file="https://gigadom.files.wordpress.com/2018/12/Untitled.png" data-orig-size="1072,229" data-medium-file="https://gigadom.files.wordpress.com/2018/12/Untitled.png?w=300" data-attachment-id="7290" data-image-meta="{" data-image-description="" src="https://gigadom.files.wordpress.com/2018/12/Untitled.png?w=450&h=144&fit=676%2C144" data-permalink="https://gigadom.in/2018/12/14/my-book-deep-learning-from-first-principlessecond-edition-now-on-amazon/untitled-129/" data-large-file="https://gigadom.files.wordpress.com/2018/12/Untitled.png?w=450&amp;h=144&amp;fit=676%2C144" alt="" data-image-title="Untitled" sizes="(max-width: 676px) 100vw, 676px" data-comments-opened="1" class="wp-image-7290" data-recalc-dims="1"></p>
<p>The <a rel="nofollow" href="https://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Daps&field-keywords=deep+learning+from+first+principles%3A+Second+edition&rh=i%3Aaps%2Ck%3Adeep+learning+from+first+principles%3A+Second+edition" target="_blank">book</a> includes the following chapters</p>
<pre>Table of Contents
Preface 4
Introduction 6
1. Logistic Regression as a Neural Network 8
2. Implementing a simple Neural Network 23
3. Building a L- Layer Deep Learning Network 48
4. Deep Learning network with the Softmax 85
5. MNIST classification with Softmax 103
6. Initialization, regularization in Deep Learning 121
7. Gradient Descent Optimization techniques 167
8. Gradient Check in Deep Learning 197
1. Appendix A 214
2. Appendix 1 – Logistic Regression as a Neural Network 220
3. Appendix 2 - Implementing a simple Neural Network 227
4. Appendix 3 - Building a L- Layer Deep Learning Network 240
5. Appendix 4 - Deep Learning network with the Softmax 259
6. Appendix 5 - MNIST classification with Softmax 269
7. Appendix 6 - Initialization, regularization in Deep Learning 302
8. Appendix 7 - Gradient Descent Optimization techniques 344
9. Appendix 8 – Gradient Check 405
References 475</pre>
<p>Also see<br>
1. <a rel="nofollow" href="https://gigadom.in/2018/06/01/my-book-practical-machine-learning-in-r-and-python-second-edition-on-amazon/" target="_blank">My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon</a><br>
2. <a rel="nofollow" href="https://gigadom.in/2017/12/16/the-3rd-paperback-edition-of-my-books-on-cricket-on-amazon/" target="_blank">The 3rd paperback &amp; kindle editions of my books on Cricket, now on Amazon</a><br>
3. <a rel="nofollow" href="https://gigadom.in/2011/11/22/de-blurring-revisited-with-wiener-filter-using-opencv/" target="_blank">De-blurring revisited with Wiener filter using OpenCV</a><br>
4. <a rel="nofollow" href="https://gigadom.in/2015/04/04/tws-4-gossip-protocol-epidemics-and-rumors-to-the-rescue/" target="_blank">TWS-4: Gossip protocol: Epidemics and rumors to the rescue</a><br>
5. <a rel="nofollow" href="https://gigadom.in/2014/08/15/a-cloud-medley-with-ibm-bluemix-cloudant-db-and-node-js/" target="_blank">A Cloud medley with IBM Bluemix, Cloudant DB and Node.js</a><br>
6. <a rel="nofollow" href="https://gigadom.in/2017/11/21/practical-machine-learning-with-r-and-python-part-6/" target="_blank">Practical Machine Learning with R and Python – Part 6</a><br>
7. <a rel="nofollow" href="https://gigadom.in/2017/01/05/googlyplus-yorkr-analyzes-ipl-players-teams-matches-with-plots-and-tables/" target="_blank">GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables</a><br>
8. <a rel="nofollow" href="https://gigadom.in/2013/06/26/fun-simulation-of-a-chain-in-android/" target="_blank">Fun simulation of a Chain in Android</a></p>
<p>To see posts click <a rel="nofollow" href="https://gigadom.in/aa-2/" target="_blank">Index of Posts</a></p>
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