Andrew Ng, the man behind the excellent ML course on coursera, has an introduction to Deep Learning using Matlab.<p>[1]: Wiki with code, exercises and explanation<p>[2]: Video lecture one with a recap on back-propagation<p>[3]: Video lecture two on Sparse Auto Encoders<p>[4]: Handouts<p>In terms of books, <i>Bayesian Reasoning and Machine Learning</i> [5] is Matlab based. So is the <i>Handbook of Monte Carlo methods</i> [6].<p>[1]: <a href="http:&#x2F;&#x2F;ufldl.stanford.edu&#x2F;wiki&#x2F;index.php&#x2F;UFLDL_Tutorial" rel="nofollow">http:&#x2F;&#x2F;ufldl.stanford.edu&#x2F;wiki&#x2F;index.php&#x2F;UFLDL_Tutorial</a><p>[2]: <a href="http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;video1.html" rel="nofollow">http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;video1.html</a><p>[3]: <a href="http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;video2.html" rel="nofollow">http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;video2.html</a><p>[4]: <a href="http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;handouts.html" rel="nofollow">http:&#x2F;&#x2F;www.stanford.edu&#x2F;class&#x2F;cs294a&#x2F;handouts.html</a><p>[5]: <a href="http:&#x2F;&#x2F;web4.cs.ucl.ac.uk&#x2F;staff&#x2F;D.Barber&#x2F;pmwiki&#x2F;pmwiki.php?n=Brml.HomePage" rel="nofollow">http:&#x2F;&#x2F;web4.cs.ucl.ac.uk&#x2F;staff&#x2F;D.Barber&#x2F;pmwiki&#x2F;pmwiki.php?n=...</a><p>[6]: <a href="http:&#x2F;&#x2F;www.maths.uq.edu.au&#x2F;~kroese&#x2F;montecarlohandbook&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.maths.uq.edu.au&#x2F;~kroese&#x2F;montecarlohandbook&#x2F;</a>