Update README.md

README.md

@@ -4,6 +4,7 @@
 2.  [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) by  Michael Nielsen (Dec 2014)
 3.  [Deep Learning](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf) by Microsoft Research (2013) 
 4.  [Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by LISA lab, University of Montreal (Jan 6 2015)
+5.  [An introduction to genetic algorithms](https://svn-d1.mpi-inf.mpg.de/AG1/MultiCoreLab/papers/ebook-fuzzy-mitchell-99.pdf)
  
 ### Courses
 
@@ -55,6 +56,7 @@ Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stan
 4.  [A Deep Learning Tutorial: From Perceptrons to Deep Networks](http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks)
 5.  [Deep Learning from the Bottom up](http://www.metacademy.org/roadmaps/rgrosse/deep_learning)
 6.  [Theano Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf)
+7.  [Neural Networks for Matlab](http://uk.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf)
 
 
 
@@ -64,6 +66,7 @@ Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stan
 1.  [deeplearning.net](http://deeplearning.net/)
 2.  [deeplearning.stanford.edu](http://deeplearning.stanford.edu/)
 3.  [nlp.stanford.edu](http://nlp.stanford.edu/)
+4.  [ai-junkie.com](http://www.ai-junkie.com/ann/evolved/nnt1.html)
 
 ### Datasets
 
@@ -74,6 +77,8 @@ Recognition](http://nlp.stanford.edu/~socherr/pa4_ner.pdf) [zip](http://nlp.stan
 5.  [Tiny Images](http://groups.csail.mit.edu/vision/TinyImages/) 80 Million tiny images6.  
 6.  [Flickr Data](http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images) 100 Million Yahoo dataset
 7.  [Berkeley Segmentation Dataset 500](http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/)
+8.  [UC Irvine Machine Learning Repository](http://archive.ics.uci.edu/ml/)
+
 
 ### Frameworks