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AI

NEWS


BOOKS

  • The book "biological sequence analysis" by Durbin and Eddy.

Programming


Cognition


Computational Science

Data Science


Data Structures

Books

  • The Algorithm Design Manual, Steven.S.Skiena - An excellent reference to algorithm design and many common data types.
  • "The Art of Computer Programming", Donald Knuth.

Graphs

Vertex

http://www.cplusplus.com/doc/tutorial/operators/ c++ http://math.stackexchange.com/questions/436070/maximum-vertex-cover http://theory.stanford.edu/~trevisan/cs261/lecture14.pdf https://wincent.com/wiki/Computing_the_Maximum_Weighted_Independent_Set_of_a_graph_path http://codereview.stackexchange.com/questions/78292/max-independent-set-of-a-sequence
http://shodhganga.inflibnet.ac.in/bitstream/10603/17548/21/21_%20appendix_02.pdf
http://stackoverflow.com/questions/8593105/maximum-independent-set-algorithm
http://iswwwup.com/t/207afe252674/max-independent-set-of-a-sequence.html
http://stackoverflow.com/questions/30921996/heuristic-to-find-the-maximum-weight-independent-set-in-an-arbritary-graph
http://stackoverflow.com/questions/30412391/maximum-independent-set-weight
http://www.wseas.org/multimedia/journals/mathematics/2012/55-232.pdf
http://ise.tamu.edu/people/faculty/butenko/yalta/slides/Trukhanov.pdf
http://www.quora.com/What-is-the-fastest-algorithm-to-enumerate-all-maximal-independent-sets-of-a-graph
http://wenda.baba.io/questions/2786226/finding-a-maximum-weight-clique-in-a-weighted-graph-c-sharp-implementation.html

http://math.stackexchange.com/questions/210160/what-is-the-node-weight-of-a-vertex
http://www.brpreiss.com/books/opus4/html/page546.html
http://codeforces.com/blog/entry/18425
http://www.boost.org/doc/libs/1_46_1/libs/graph/example/undirected.cpp http://www.quora.com/How-can-I-implement-a-weighted-directed-graph-in-C++-or-Java-using-object-oriented-programming http://www.sanfoundry.com/cpp-programming-examples-graph-problems-algorithms/ https://quickgrid.wordpress.com/2015/05/30/inputting-directed-undirected-weighted-and-unweighted-graph-in-c-c-adjacency-matrix/ http://stanford.edu/~stepp/cppdoc/BasicGraph-class.html https://hal.archives-ouvertes.fr/file/index/docid/905079/filename/preprint-AndreicaM-TowardsRealTimeSchedulingTreeNetworks.pdf
https://en.wikipedia.org/wiki/Independent_set_%28graph_theory%29
http://stackoverflow.com/questions/6798953/program-of-recursion-for-maximum-independent-set-in-a-graph
https://wincent.com/wiki/Computing_the_Maximum_Weighted_Independent_Set_of_a_graph_path
http://www.geeksforgeeks.org/largest-independent-set-problem/ http://www.gdeepak.com/thesisme/thesis-Choosing%20the%20Efficient%20Algorithm%20for%20Vertex%20Cover%20problem.pdf with c++ code for MVC https://github.com/jcoupey/mwis http://www.cise.ufl.edu/~sahni/dsaac/public/chapters/c20.pdf to read http://coderclub.com/developerworks/library/os-giraph/ to read https://www.cs.berkeley.edu/~vazirani/algorithms/chap6.pdf https://github.com/networkx/networkx/tree/330fd210d29f5ca4d8f3bf9019cb4912b9f62fbe/doc/source to search http://makepp.sourceforge.net/1.19/makepp_tutorial.html



Neural Networks

Recurrent Neural Networks

Neural Networks with ML - Syllabus

Lecture 1: Introduction Lecture 2: The Perceptron learning procedure Lecture 3: The backpropagation learning procedure Lecture 4: Learning feature vectors for words Lecture 5: Object recognition with neural nets Lecture 6: Optimization: How to make the learning go faster Lecture 7: Recurrent neural networks Lecture 8: More recurrent neural networks Lecture 9: Ways to make neural networks generalize better Lecture 10: Combining multiple neural networks to improve generalization Lecture 11: Hopfield nets and Boltzmann machines Lecture 12: Restricted Boltzmann machines (RBMs) Lecture 13: Stacking RBMs to make Deep Belief Nets Lecture 14: Deep neural nets with generative pre-training Lecture 15: Modeling hierarchical structure with neural nets Lecture 16: Recent applications of deep neural nets


Parallel


NUMA