Systems and Programs.txt

###########
### general

# List of Programming Resources and Books
http://resrc.io/list/10/list-of-free-programming-books/#algorithms--datastructures
http://resrc.io/list/10/list-of-free-programming-books/#python


# 100 Most Influential Books According to Stack Overflow
http://cspray.github.io/2012/02/13/a-community-gone-mad.html
http://cspray.github.io/my.so-archive/100-most-influential-programming-books.html


# online interpreters
http://repl.it/languages/



#########
### tools

# ScreenToGif record comp screen and save as animated Gif
https://screentogif.codeplex.com/

http://www.discourse.org/



#########
### computer science artificial intelligence machine learning non-finance

# Prisma program neural network can redraw pictures in the style of paintings by well-known artists
http://www.bloomberg.com/view/articles/2016-06-20/new-masters-software-has-painterly-technique-down-pat
http://phys.org/news/2016-06-russian-photo-app-prisma-splash.html
The Prisma neural network can separate the style and technique of a painting from its content (people, trees, etc.)
The Prisma program is able to paint a given person's face in the style of Picasso or Van Gogh. 
Prisma was written by the Russian programer Alexei Moiseenkov, using research from German universities. 
The Prisma program has reached the no. 1 spot in the iOS app store. 

# University of Pennsylvania statistician Richard Berk created machine learning algorithm that predicts likelihood someone will commit domestic violence crimes
http://www.bloombergview.com/articles/2016-03-08/the-crime-you-have-not-yet-committed
The algorithm uses data such as age, sex, zip code, age at first arrest, list of previous charges like drunk driving, animal mistreatment, and firearms crimes. 
The algorithm doesn't use race directly but race can be inferred from other data like zip code.
The algorithm outperforms parole judges, with about 20% of those released by judges were later arrested, while only 10% of the computer’s choices were arrested.
Philadelphia uses the algorithm to assign parolees to high, medium, and low-risk groups, allowing parole officers to focus most of their attention on the high-risk cases.

# machine learning classification algorithms are easily fooled by adversarial examples
Szegedy ANN Deep Learning Adversarial Misclassification.pdf
http://www.kdnuggets.com/2015/07/deep-learning-adversarial-examples-misconceptions.html
http://www.kdnuggets.com/2014/06/deep-learning-deep-flaws.html
Adversarial examples are images (data sets) that are slightly modified in such a way that machine learning classification algorithms are fooled into classifying them incorrectly.
Adversarial examples are often indistinguishible by the human eye from the original images, yet are incorrectly classified by machine learning algorithms.
The human eye (perception) is not susceptible to adversarial examples that are able to fool machine learning algorithms.
Deep learning is not the only machine learning algorithm susceptible to adversarial examples, and it's not more susceptible than other algorithms.
Rubbish examples are random images that algorithms classify to some class with high confidence, even though they should not belong to any class. 
Machine learning algorithms often classify a very large percentage of random images to some class with high confidence, even though they should not belong to any class. 
The mis-classification of adversarial examples by machine learning algorithms may be caused by the linear nature of the algorithms. 
Machine learning algorithms classify images (data sets) in a very different way than human (animal) perception does. 
Human (animal) perception performs abstractions of images by identifying edges of objects, and by identifying objects contained in images.  
Human (animal) perception classifies images based on the objects (elements) they contain.  
A face for example has two eyes, a nose and a mouth.
But edge detection is a nonlinear process, so linear algorithms can't identify the edges of objects. 
Machine learning classification algorithms don't create abstractions of images (data sets), the way human (animal) perception does. 
Instead, algorithms classify images based on pixels, so they're susceptible to small changes of individual pixels.

# Intelligent learning systems are composed of two complementary sub-systems
http://techxplore.com/news/2016-06-insights-human-foster-smarter-artificial.html
The first system acquires knowledge and skills from exposure to experiences, while the second system stores and organizes the experiences so that they can be replayed, and integrated back into the first system. 
In animal brains the first system is the frontal cortex, and the second system is the hippocampus. 

# Defective lists the major defects of C++
http://yosefk.com/c++fqa/defective.html

# Ray Solomonoff invented algorithmic probability related to Kolmogorov complexity and algorithmic information theory
https://en.wikipedia.org/wiki/Ray_Solomonoff
Algorithmic probability can predict the most likely next event in a series of events, using inductive inference (Bayes' rule).

# Laszlo Babai claims to have proven that P=NP
http://www.chicagomag.com/city-life/October-2015/Why-Computer-Scientists-and-Mathematicians-Are-Stunned-By-a-Chicago-Professors-New-Proof/
That means that every problem whose solution can be quickly verified, 
can also be quickly solved. 

# Does P equal NP? problem
http://news.mit.edu/2009/explainer-pnp
Does every problem whose solution can be quickly verified, can also be quickly solved?
P-problems have solutions that are polynomial in number of elements.  
For example sorting a list is a P-problem. 
NP-problems have solutions that are non-polynomial (exponential) in number of elements.
Some of them may also have solutions in polynomial time, but nobody knows if they do. 
For example finding the prime factors of a number is a NP-problem. 
Verifying a solution is a forward process (for example multiplication).  
Finding a solution is an inverse process (for example division or integration).  
"Does P equal NP?" means that if a solution can be verified in polynomial time, 
then can the solution also be found in polynomial time?
In other words, can efficient solutions to NP-problems ever be found? 

# Italian computer scientist Massimo Marchiori invented Hyper Search algorithm inspiration for Google's PageRank
http://www.bloomberg.com/news/2014-05-15/meet-the-italian-man-who-beat-google-to-web-search-and-gave-it-away-for-free-.html

# Exquisite Forest algo generates music
http://labs.dinahmoe.com/theme/
http://www.exquisiteforest.com/
# Tate
http://milk.co/forest-tate

# machine learning boom of 2015
http://www.bloomberg.com/news/articles/2015-12-08/why-2015-was-a-breakthrough-year-in-artificial-intelligence

# Google RankBrain machine learning system trying to understand language
http://www.bloomberg.com/news/articles/2015-10-26/google-turning-its-lucrative-web-search-over-to-ai-machines
RankBrain parses text on websites, and ranks websites nased on their relevance for certain queries.

# NEAT algorithm for evolving neural networks and simulating creative processes
http://fivethirtyeight.com/features/stop-trying-to-be-creative/
Creative processes are driven by a hazy intuition or vision, instead of a plan.
Creative processes are deliberate, not just random searches, and rely on serendipity.
Creative processes can't be planned or predicted ahead of time because they don't have a well defined goal, so the time to arrive at their objective can't be known in advance.
Pursuing creative solutions instead of well defined goals is therefore risky, since many attempts will not pay off, but the potential payoffs are higher as well.
# evolve pictures using genetic programming
http://picbreeder.org/

# machine learning boom funded by startups
http://www.bloomberg.com/news/articles/2015-02-03/i-ll-be-back-the-return-of-artificial-intelligence
Most AI startups aren’t developing machines capable of animal intelligence, but are instead making clever tools to solve practical data analysis problems. 
The extraordinary increase in chip performance over the last 17 years has allowed applications of machine learning software.
Video chips in 2014 became 84.3 times more powerful than those in 2004.

# Hofstadter The Man Who Would Teach Machines to Think
http://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/
AI today has become too much like the man who tries to get to the moon by climbing a tree: One can report steady progress, all the way to the top of the tree.

# Is A Simulated Brain Conscious?
http://www.popsci.com/article/science/simulated-brain-conscious

# Human brains combined with computers
http://online.wsj.com/articles/a-lesson-from-alan-turing-how-creativity-drives-machines-1411749814
Ada Lovelace envisioned that the combined talents of humans and computers will indefinitely be more creative than any computer working alone.
Licklider wrote in 1960: Human brains and computing machines will be coupled together very tightly, and the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.

# machine learning experts in high demand
http://www.wsj.com/article_email/artificial-intelligence-experts-are-in-high-demand-1430472782-lMyQjAxMTA1MzA2MTMwNTEyWj

# kaggle: selecting right data much more important than sophisticated models
http://www.nytimes.com/2015/03/22/opinion/sunday/making-march-madness-easy.html
Ensemble prediction algorithms often do better than their components do alone.

# machine learning at Google
http://www.wired.com/2014/07/google_brain/

http://www.wired.com/2013/12/facebook-yann-lecun-qa/
Deep learning was started by Geoff Hinton, Yann LeCun, and Yoshua Bengio, from the University of Montreal. 
They got funding from the CIFAR foundation (Canadian Institute For Advanced Research). 
Geoff was the director, and Yann LeCun was the chair of the advisory committee, and they would meet twice a year to discuss progress.

# Machine Learning Most Popular Course At Stanford
http://www.forbes.com/sites/anthonykosner/2013/12/29/why-is-machine-learning-cs-229-the-most-popular-course-at-stanford/

# machine learning replacing human workers
http://www.bloomberg.com/news/2014-03-12/your-job-taught-to-machines-puts-half-u-s-work-at-risk.html
Three associates selected relevant documents from a smaller sample, "teaching" their reasoning to the computer. The software’s algorithms then sorted the remaining material by importance.
http://www.bloomberg.com/infographics/2014-03-12/job-automation-threatens-workforce.html

# machine learning scores consumer credit
http://www.bloomberg.com/news/2014-10-01/lender-charging-390-uses-data-to-screen-out-deadbeats.html
An algorithm called Naive Bayes checks whether individual traits such as age or marital status help predict defaults.
Random forests place borrowers in groups and looks for patterns to emerge.
Hidden Markov models analyze whether events such as lapsed mobile-phone payments, signal an unseen condition such as illness.

# robot learning through crowdsourcing imitation, demonstration
http://www.kurzweilai.net/crowdsourcing-for-robots
http://www.washington.edu/news/2014/06/26/ask-the-crowd-robots-learn-faster-better-with-online-helpers/
http://news.nationalgeographic.com/news/2013/07/130719-robot-lfd-pr2-artificial-intelligence-crowdsourcing-robotics-machine-learning/
http://www.scienceworldreport.com/articles/15704/20140627/robots-learn-faster-with-online-input.htm
# humans teaching computers: Mechanical Turk online computer training
https://www.mturk.com/mturk/welcome

# Robots That Learn Through Repetition, Not Programming
http://www.technologyreview.com/news/530871/robots-that-learn-through-repetition-not-programming/

# robots that collaborate with people
http://www.kurzweilai.net/developing-robots-that-collaborate-with-people
http://www.economist.com/news/technology-quarterly/21584455-robotics-new-breed-robots-being-designed-collaborate-humans
http://www.technologyreview.com/news/530696/how-human-robot-teamwork-will-upend-manufacturing/
http://news.discovery.com/tech/robotics/robot-human-crosstraining-130218.htm

# machine learning began dominating traditional AI
http://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/

# Artificial Intelligence programs write news articles from data - automated narrative generation
http://www.nytimes.com/2015/03/08/opinion/sunday/if-an-algorithm-wrote-this-how-would-you-even-know.html
Artificial Intelligence programs identify patterns, trends, and anomalies, and then create explanatory narrative.
The Associated Press uses Automated Insights’ Wordsmith platform to create more than 3,000 financial reports per quarter.
Kristian Hammond estimates that 90% of news could be algorithmically generated by the mid-2020s
# Narrative Science programs  write sports and finance articles
http://www.wired.com/2012/04/can-an-algorithm-write-a-better-news-story-than-a-human-reporter/
Kristian Hammond co-founded Narrative Science.
Human meta-writers compile the vocabulary and create a framework for articles.
Clients customize the tone of the stories.
# Philip Parker AI programs wrote over 1 Million Books
http://www.huffingtonpost.com/2013/02/11/philip-parker-books_n_2648820.html
http://www.businessinsider.com/novels-written-by-computers-2014-11
http://www.narrativescience.com/quill
# Robot Journalists: people can't tell apart sports stories written by computers from those written by human writers
http://www.tandfonline.com/doi/pdf/10.1080/17512786.2014.883116

# Benjamin Bratton: AI has little to do with human or animal intelligence
http://opinionator.blogs.nytimes.com/2015/02/23/outing-a-i-beyond-the-turing-test/
Why define an advanced A.I. by its resemblance to ours?
Human intelligence simply can’t exhaust the possibilities of intelligence.
We need a view of A.I. that is less narcissistic, more than simply a machine version of ourselves.
The criteria for being "intelligent" doesn’t have have to be how it reflects humanness back at us. 
Biomorphic imitation is not how we design complex technology. 
Airplanes don’t fly like birds fly, 
Whether airplanes fly like birds isn't a test to determine if planes can "really" fly. 
Programming A.I. not to "harm humans" (inspired by Isaac Asimov) makes sense only when an A.I. knows what humans are and what harming them means. 
Even if A.I. has no malevolence towards us, it might harm us by following exactly our well-meaning instructions to an idiotic and catastrophic extreme. 

# The Second Machine Age
http://www.mckinsey.com/Insights/Strategy/Artificial_intelligence_meets_the_C-suite
The Industrial Revolution was when humans overcame the limitations of our muscle power. 

# The Steely, Headless King of Texas Hold ’Em
http://www.nytimes.com/2013/09/08/magazine/poker-computer.html



#########
### blogs

# Zygmunt Zajac economist and machine learning expert
http://fastml.com/
https://github.com/zygmuntz

# Pawel Lachowicz Python quant
http://www.quantatrisk.com/

# Grzegorz Chrupala computational linguist and machine learning expert
http://grzegorz.chrupala.me/

# Triskelion kaggle Master and machine learning expert
http://mlwave.com/

http://datalligence.blogspot.com/

# Gabriel Synnaeve kaggle Master from LSCP and INRIA
http://snippyhollow.github.io/
http://snippyhollow.github.io/blog/
# good links and papers
http://snippyhollow.github.io/blog/2014/08/09/so-you-wanna-try-deep-learning/
# simple Python deep neural network with or w/o dropout
https://gist.github.com/SnippyHolloW/8a0f820261926e2f41cc

# Carl Vogel - converting R to Python - NY economist, quant, HF finance data
http://slendermeans.org/
http://slendermeans.org/category/will-it-python.html
https://github.com/carljv/Will_it_Python

# Scott Fortmann-Roe: out of sample prediction error
http://scott.fortmann-roe.com/docs/MeasuringError.html
# Scott Fortmann-Roe: bias-variance tradeoff
http://scott.fortmann-roe.com/docs/BiasVariance.html

# Nathan Yau - FlowingData data exploration, analysis, and visualization
http://flowingdata.com/

# stackexchange blogs
http://stackexchange.com/blogs
http://stats.blogoverflow.com/
http://security.blogoverflow.com/

# Jason Bryer: R, Jekyll, web publishing plus multiple R packages
http://jason.bryer.org/
http://jbryer.github.com
https://github.com/jbryer
# github hosted page setup
https://pages.github.com/
# website modified from Skeleton, hosted on Github Pages using Jekyll
http://jason.bryer.org/about.html
# Using (R) Markdown, Jekyll, & GitHub for a Website
http://jason.bryer.org/posts/2012-12-10/Markdown_Jekyll_R_for_Blogging.html
http://www.getskeleton.com/



#########
### education

# New York R Conference April 8-9, 2016
http://www.rstats.nyc/index.html

# ScriptScoop aggregator of code examples
http://www.scriptscoop.com/

# StackOverflow flaws
http://michael.richter.name/blogs/why-i-no-longer-contribute-to-stackoverflow

# Knewton personalized online courses, figure out what each student knows and how that student learns best, then recommends what to study next.
http://www.knewton.com/

# DataCamp & rcademy - Jonathan Cornelissen (works with Boudt)
https://www.datacamp.com/

# data science projects and instruction
http://www.teamleada.com/

# Software Carpentry sponsored by RStudio: volunteer instructors for teaching researchers computing skills
http://software-carpentry.org/
http://software-carpentry.org/blog/

# Caltech machine learning online courses
https://work.caltech.edu/telecourse.html
https://www.coursera.org/course/ml

# Metacademy graphs of knowledge dependency by John Langford, Roger Grosse, and Colorado Reed
http://metacademy.org/
http://metacademy.org/graphs/concepts/feed_forward_neural_nets
http://metacademy.org/roadmaps/
# username (public): algoquant
# email (not public): algoquant@algoquants.ch
http://hunch.net/?p=2714
http://www.cs.toronto.edu/~rgrosse/
http://obphio.us/

# online courses
https://www.edx.org/
https://www.udacity.com/

# MOOC with interactive content by David Cox Professor of Molecular and Cellular Biology and Computer Science
http://www.mcb80x.org/

# thinkful online mentoring course Python, Ruby, AngularJS
http://www.thinkful.com/

# Matrix Reference Manual
http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html

# C++ introduction
https://tfetimes.com/beginners-guide-to-c/
https://tfetimes.com/crash-course-in-c/



#########
### social learning

# Dream to Learn social learning platform
https://dreamtolearn.com/
https://dreamtolearn.com/content/about.html
# Ryan Anderson - build a social platform for learning, development and growth
https://dreamtolearn.com/ryan
https://dreamtolearn.com/ryan/data_analytics_viz/71
https://dreamtolearn.com/node/9H0SX6Y7KX740GNSX4IFIBFZW



#########
### machine learning

Machine learning refers to creating statistical models that fit into some set of data.  Linear regression is an example of a machine learning model.  Machine learning uses several techniques that make it more powerful:
First, it uses cross-validation (aka backtesting), or testing of the model on out-of-sample data.
Second, it uses regularization, or model parameter constraints, to avoid so-called overfitting. 
Third, it uses ensemble learning, by combining the results of many models to achieve better performance than any single model.

# good intro for beginners
http://machinelearningmastery.com/
http://machinelearningmastery.com/blog/
http://machinelearningmastery.com/how-to-get-started-with-machine-learning-algorithms-in-r/

# very simple intros
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
http://www.datasciencecentral.com/profiles/blogs/a-tour-of-machine-learning-algorithms

# "Statistical Modeling: The Two Cultures" by Leo Breiman: statistics vs. machine learning
http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/
http://stats.stackexchange.com/questions/6/the-two-cultures-statistics-vs-machine-learning
"The biggest difference is that statistics emphasizes inference, whereas machine learning emphasized prediction."
"In statistics, you infer the process by which the data was generated."
"In machine learning, you predict what future data will look like w.r.t. some variable."
"You can still make good predictions when p >> n, but you can't make very good inferences about what variables are actually important and why."
http://www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared

# Andrew Gelman likes machine learning
http://www.stat.columbia.edu/~gelman/
http://andrewgelman.com/2005/09/23/classparticipat/

# Data Science Toolkit
http://www.datasciencecentral.com/profiles/blogs/the-data-science-toolkit-my-boot-camp-ciriculum

# MXNet is a deep learning framework
http://mxnet.readthedocs.org/en/latest/
https://github.com/dmlc/mxnet
http://mxnet.readthedocs.org/en/latest/R-package/index.html

# Leo Breiman from Berkeley invented random forests
http://www.stat.berkeley.edu/~breiman/RandomForests/
http://www.stat.berkeley.edu/~breiman/sf.html
http://www.stat.berkeley.edu/~breiman/


#########
### machine learning software

# Machine Learning Open source (MLOSS)
http://mloss.org/software/
http://mloss.org/community/
# Machine Learning Open source

# MLOSS is run by:
http://sonnenburgs.de/soeren/category/
http://blog.mikiobraun.de/
http://www.ong-home.my/

# RapidMiner environment for machine learning, data mining, and predictive analytics
http://rapidminer.com/
http://rapidminer.com/rapidminer-and-r/
http://www.aphysicistinwallstreet.com/2010/11/example-rapidminer-r-for-trading.html
# R and RapidMiner for time series forecasting
http://www.simafore.com/blog/bid/204923/Combining-power-of-R-and-RapidMiner-for-time-series-forecasting
http://www.kdnuggets.com/2010/11/rapidminer-r-extension.html

### Shogun machine learning toolbox, Support Vector Machines (SVM)
# from Berlin Institute of Technology
http://www.shogun-toolbox.org/
http://www.shogun-toolbox.org/doc/en/3.0.0/index.html
http://en.wikipedia.org/wiki/Shogun_(toolbox)
https://github.com/shogun-toolbox/shogun/
http://sonnenburgs.de/soeren/category/
https://github.com/lisitsyn
https://github.com/vigsterkr
# R Static Interface Examples
http://www.shogun-toolbox.org/doc/en/3.0.0/r_static_examples.html
# R Modular Interface Examples
http://www.shogun-toolbox.org/doc/en/3.0.0/r_modular_examples.html

# scikit-learn by INRIA David Cournapeau
http://scikit-learn.org/stable/index.html
http://en.wikipedia.org/wiki/Scikit-learn
# scikit-learn by INRIA Olivier Grisel
http://ogrisel.com/

# Machine Learning in Python - Trento
http://mlpy.sourceforge.net/

# Support Vector Machine Python
http://www.yaksis.com/posts/why-use-svm.html

# Machine Learning on cloud
https://bigml.com/
http://en.wikioffuture.org/BigML/



#########
### neural networks

# Hacker's guide to Neural Networks
http://karpathy.github.io/neuralnets/

# Andrej Karpathy: Recurrent Neural Networks
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://cartesianfaith.com/2016/02/23/is-deep-learning-a-markov-chain-in-disguise/
http://cs.stanford.edu/people/karpathy/

# Michael Nielsen book on artificial neural networks and deep learning
http://michaelnielsen.org/blog/michael-a-nielsen/
http://michaelnielsen.org/blog/
http://www.michaelnielsen.org/ddi/
https://github.com/mnielsen
# Reinventing explanation
http://michaelnielsen.org/reinventing_explanation/index.html
# Reinventing Discovery book on collective intelligence and open science
http://www.amazon.com/gp/product/0691148902

# Neural Networks course Michael Biehl
http://www.cs.rug.nl/~biehl/Teaching/NN/index.html
# Pattern Recognition course Michael Biehl
http://www.cs.rug.nl/~biehl/Teaching/PR/

# Toronto ANN course
https://www.coursera.org/course/neuralnets

# Data Science Toolbox environment in Ubuntu Linux includes Python and R
http://datasciencetoolbox.org/
https://github.com/DataScienceToolbox/data-science-toolbox/
http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html

# GraphLab Create machine learning Python API system
http://graphlab.com/
https://dato.com/
https://github.com/graphlab-code/graphlab

# John Langford: Machine Learning Theory
http://hunch.net/
# Metacademy: a package manager for knowledge
http://hunch.net/?p=2714
http://metacademy.org/roadmaps/rgrosse/deep_learning

# Structural Risk Minimization
http://hunch.net/~jl/projects/prediction_bounds/thesis/mathml/thesisse19.xml

# Avoid Machine Learning Mistakes article
https://medium.com/@nomadic_mind/new-to-machine-learning-avoid-these-three-mistakes-73258b3848a4

# Kevin P. Murphy book Machine Learning: Probabilistic Perspective with Matlab code
http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020
http://www.cs.ubc.ca/~murphyk/MLbook/
https://github.com/probml



#########
### deep learning

# getting started
http://snippyhollow.github.io/blog/2014/08/09/so-you-wanna-try-deep-learning/
https://gist.github.com/SnippyHolloW/8a0f820261926e2f41cc
https://github.com/SnippyHolloW

http://deeplearning.net/

# Deep Learning: Courses, Tutorials, Software
http://www.kdnuggets.com/2014/05/learn-deep-learning-courses-tutorials-overviews.html
# primer on deep learning
http://www.datarobot.com/blog/a-primer-on-deep-learning/
# Unsupervised Feature Learning and Deep Learning (UFLDL) Tutorial
http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial

# interview with Yann LeCun of NYU
# Deep Learning was developed by Geoff Hinton (Toronto, Google), Yann LeCun (NYU, Facebook), and Yoshua Bengio (Montreal).
http://www.wired.com/2013/12/facebook-yann-lecun-qa/

http://www.kdnuggets.com/2014/02/exclusive-yann-lecun-deep-learning-facebook-ai-lab.html
# Deep learning two main packages:  Torch7, and Theano + Pylearn2
# Torch7 is an extension of the LuaJIT language (compiled version of Lua language), which is extremely fast.
# Theano+Pylearn2 has the disadvantage of using Python (it's slow). 

# deep learning sparse filtering Kaggle: Black Box Challenge
http://cs.stanford.edu/~jngiam/
http://fastml.com/deep-learning-made-easy/

# Deep Learning and the Renormalization Group Works
https://www.linkedin.com/profile/view?id=1259300
https://charlesmartin14.wordpress.com/2015/03/25/why-does-deep-learning-work/
https://charlesmartin14.wordpress.com/2015/04/01/why-deep-learning-works-ii-the-renormalization-group/

# Hannes Bretschneider: PhD Student Neural networks & Deep Learning University of Toronto
https://ha.nnes.io/
https://github.com/hannes-brt
# GPU Deep Learning Library in Python
https://github.com/hannes-brt/hebel

# commercial Numba to program the GPU from Python
http://continuum.io/webinars/using-numba-program-gpu
https://store.continuum.io/cshop/accelerate/
https://developer.nvidia.com/how-to-cuda-python
http://devblogs.nvidia.com/parallelforall/numbapro-high-performance-python-cuda-acceleration/

# Running Python on GPUs
http://fastml.com/running-things-on-a-gpu/

# Machine Learning consultants and examples (Matlab, Python, VBA)
http://www.sitmo.com/

# Artificial Intelligence: A Modern Approach
http://aima.cs.berkeley.edu/

http://bayesia.us/


### articles

# deep learning degrades quickly as art becomes more abstract
https://medium.com/the-physics-arxiv-blog/computer-vision-algorithms-detect-human-figures-in-cubist-art-e82995bb42a0



#########
### machine learning cloud

# H2O machine learning with R API - Matt Dowle
http://www.h2o.ai/
http://www.h2o.ai/docs/
http://www.h2o.ai/download/h2o/r
http://www.h2o.ai/use-cases/predictive-modeling-factories/

# H2O University
http://university.h2o.ai/?_ga=1.199135535.147602298.1461338879

# H2O prediction engine, sponsored by Stephen Boyd, Rob Tibshirani, Trevor Hastie
http://0xdata.com/
http://0xdata.com/downloadtable/
http://docs.0xdata.com/
https://github.com/0xdata
http://0xdata.github.io/h2o/
http://0xdata.com/blog/2014/06/h2o-killer-application-spark/

# yhat Python and R model hosting
http://www.yhathq.com/
http://blog.yhathq.com/
https://github.com/yhat
# random forests in R and Python hosted on yhat
http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html
# yhat Sciencebox R and Python server with RStudio
https://yhathq.com/products/sciencebox
http://venturebeat.com/2014/06/17/yhat-sciencebox/

# PredictionIO with either PHP, Python, or Ruby code
http://prediction.io/
https://github.com/PredictionIO
http://docs.prediction.io/current/

# Machine Learning on cloud
https://bigml.com/
http://en.wikioffuture.org/BigML/

# Machine Learning for marketing
http://www.wise.io/

# cloud ML using fora functional language
http://www.ufora.com/
http://en.wikibooks.org/wiki/FORA/tutorial
FORA programs dynamically scale in parallell to use all the computer resources available to minimise runtime.
FORA interpreter dynamically compiles code so that execution efficiency is similar to a program written in C.



#########
### kaggle ML prediction competitions

# kaggle ML prediction competitions
https://www.kaggle.com/
https://www.kaggle.com/wiki/KaggleMemberFAQ
http://blog.kaggle.com/
http://www.forbes.com/sites/parmyolson/2012/09/13/worlds-top-data-scientists-open-doors-to-big-cash-contests/

# DataRobot by Xavier Conort - kaggle Master
http://www.datarobot.com/
http://www.datarobot.com/blog/
https://www.kaggle.com/users/17379/xavier-conort

# kaggle blog
http://blog.kaggle.com/
# Kaggle Masters - how they do it
http://blog.kaggle.com/2014/08/01/learning-from-the-best/
"Friedman’s gradient boosting machine works great" - gbm package

# kaggle masters
https://www.kaggle.com/users
https://www.kaggle.com/wiki/KaggleMemberFAQ
https://www.linkedin.com/pub/owen-zhang/51/aa0/363
http://www.kaggle.com/users/7756/owen

# kaggle Titanic Competition using R
http://www.joyofdata.de/blog/titanic-challenge-kaggle-svms-kernlab-decision-trees-party/
https://www.kaggle.com/users/130117/curt-wehrley
https://www.kaggle.com/c/titanic-gettingStarted/details/new-getting-started-with-r
https://github.com/wehrley/wehrley.github.io/blob/master/SOUPTONUTS.md
# Solution using Random Forests
http://trevorstephens.com/post/72916401642/titanic-getting-started-with-r
https://github.com/trevorstephens/titanic/commit/d2dfe4ef258c9f17efc911f33eaecb2fa3c8faca

# Russian machine learning cloud
http://www.octopuscip.comcastbiz.net/


### Machine Learning courses

# udacity Machine Learning courses
https://www.udacity.com/course/ud675
https://www.udacity.com/course/ud741
https://www.udacity.com/course/ud820

# Stanford Machine Learning course
https://www.coursera.org/course/ml
https://class.stanford.edu/dashboard
https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/about
https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/wiki/StatLearning/
https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/progress
https://class.stanford.edu/courses/HumanitiesScience/StatLearning/Winter2014/courseware/d9820868fd3642f19ee45e273e3dfafa/58bee9a364d14f6d9306f0e554acf46c/
# Stanford Machine Learning course using Octave-Forge
http://openclassroom.stanford.edu/MainFolder/HomePage.php
http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

# Pedro Domingos UWash machine learning coursera
http://homes.cs.washington.edu/~pedrod/
https://www.coursera.org/course/machlearning

# IPython Scikit machine learning examples tutorials
http://ipython-books.github.io/featured-04/
http://amueller.github.io/sklearn_tutorial/#/
http://nbviewer.ipython.org/github/gmonce/scikit-learn-book/tree/master/
https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks
http://nbviewer.ipython.org/github/agconti/kaggle-titanic/blob/master/Titanic.ipynb
http://www.datasciencecentral.com/forum/topics/interesting-tutorials-from-pycon-2014-usa?groupUrl=resources
http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/tree/master/
http://nbviewer.ipython.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb
http://pyvideo.org/video/2561/exploring-machine-learning-with-scikit-learn
http://pyvideo.org/video/2614/diving-deeper-into-machine-learning-with-scikit-l


#########
### Machine Learning resources

# good websites
http://www.datasciencecentral.com/
http://www.analyticbridge.com/

# Machine Learning Resources
http://www.datasciencecentral.com/group/resources/forum/topics/comprehensive-list-of-data-science-resources
http://www.datasciencecentral.com/group/resources/forum/topics/a-large-set-of-machine-learning-resources-for-beginners-to-mavens

# Big data sets available for free
http://www.datasciencecentral.com/group/resources/forum/topics/big-data-sets-available-for-free
# Tutorials from PyCon 2014 – USA
http://www.datasciencecentral.com/group/resources/forum/topics/interesting-tutorials-from-pycon-2014-usa
# how to post your free stuff
http://www.datasciencecentral.com/group/resources/forum/topics/free-stuff-for-data-science-publishers-authors-bloggers-professor



#########
### Python

# Python documentation
https://docs.python.org/3/

### Python learning

# Codecademy Python course
http://www.codecademy.com/tracks/python

# Google Python Class
https://developers.google.com/edu/python/

# Python tutorials
https://docs.python.org/3.5/tutorial/
http://www.learnpython.org/
http://www.diveintopython.net/

# Udacity Intro to Computer Science with Python certificate (pay)
https://www.udacity.com/course/cs101

# Python Coursera certificate (pay)
https://www.coursera.org/course/interactivepython1

# Python O’Reilly School of Technology certificate (pay)
http://www.oreillyschool.com/certificate-programs/python-programming/

# Python Course
http://software-carpentry.org/

# Python learning resources
http://resrc.io/list/10/list-of-free-programming-books/#python

# Python interpreter
http://repl.it/languages/Python

# Anaconda: Python analytics, scientific computing, and data visualization
http://continuum.io/
# install Anaconda: Python + Data Science - Quick Start Guide
http://bigdata.braccialli.net/2014/06/python-for-data-science-quick-start.html

# Python missing data debate
http://www.numpy.org/NA-overview.html
# Python doesn't have missing data types
http://www.activeanalytics.co.uk/blog/rvspythonwhyrisstillthekingofstatisticalcomputing



#########
### IPython interactive computing

http://ipython.org/
# IPython notebooks
http://ipython.org/notebook.html
https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks
# IPython vs knitr, or Python vs R
http://yihui.name/en/2012/11/ipython-vs-knitr/

# share IPython notebooks
http://nbviewer.ipython.org/

# Using R Within the IPython Notebok
http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Builtin%20Extensions/R%20Magics.ipynb



#########
### Python scientific computing

http://www.numpy.org/
http://www.slideshare.net/zukun/scientific-programming-in-python
http://fperez.org/py4science/index.html
http://fperez.org/py4science/starter_kit.html

# SimPy Python for simulations
https://simpy.readthedocs.org/en/latest/
http://en.wikipedia.org/wiki/SimPy

# Python pandas - Wes McKinney
http://pandas.pydata.org/index.html
http://blog.wesmckinney.com/

# Pandas tutorials
http://pandas.pydata.org/pandas-docs/stable/tutorials.html
http://www.bearrelroll.com/2013/05/python-pandas-tutorial/
http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/

# data wrangling with Pandas
http://nbviewer.ipython.org/github/fonnesbeck/pytenn2014_tutorial/blob/master/Part%201.%20Data%20Wrangling%20with%20Pandas.ipynb?create=1

# Python for research
http://www.stat.washington.edu/~hoytak/blog/whypython.html

# Python Data Analysis Library

# Python Wakari.IO Web-based Python Data Analysis
https://wakari.io/


### Python discussions
# C++/Python/R alternatives
http://quant.stackexchange.com/questions/728/switching-from-c-to-r-limitations-applications

# Why is SciPy better than Matlab or IDL?
https://groups.google.com/forum/#!topic/scipy-user/DrFRcJWWPJo

# Python is steadily eating other languages’ lunch
http://www.talyarkoni.org/blog/2013/11/18/the-homogenization-of-scientific-computing-or-why-python-is-steadily-eating-other-languages-lunch/

# Top Languages for analytics, data mining, data science
http://www.kdnuggets.com/2013/08/languages-for-analytics-data-mining-data-science.html?goback=%2Enpp_matthew*5dowle%2F8%2F435%2F512%2Eanb_155029_*2_*1_*1_*1_*1_*1#%21
# R and Python
http://www.linkedin.com/groupItem?view=&gid=155029&type=member&item=68240217&qid=60beb1ba-cd0c-477f-bbd3-49d5d5b40bc3&trk=groups_most_popular-0-b-ttl&goback=%2Enpp_matthew*5dowle%2F8%2F435%2F512%2Eanb_155029_*2_*1_*1_*1_*1_*1%2Egmp_155029

# Index Python Packages
https://pypi.python.org/pypi

# Django Python Web framework
https://www.djangoproject.com/



#########
### Python finance

# Python quants
http://tpq.io/
http://pqp.io/
https://www.linkedin.com/pulse/open-source-quantitative-finance-neil-fowler


#########
### Jupyter Notebooks

# tutorial Jupyter Notebooks
https://github.com/michhar/useR2016-tutorial-jupyter

# Jupyter hub
http://jupyterhub.readthedocs.io/en/latest/index.html


#########
### data science

# websites
http://www.datasciencecentral.com/

# Data Science Specialization
http://datasciencespecialization.github.io/
https://github.com/DataScienceSpecialization/DataScienceSpecialization.github.io#contributing

# data science compared to other analytic disciplines
http://www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared
# Life Cycle of Data Science Projects
http://www.datasciencecentral.com/profiles/blogs/life-cycle-of-data-science-projects

# Becoming a Data Scientist – Curriculum via Metromap
http://nirvacana.com/thoughts/becoming-a-data-scientist/

# Data Wrangler
http://vis.stanford.edu/wrangler

# Rules for Scientific Data
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003542

# FigShare repository: citable, shareable, and discoverable
http://figshare.com

# Zenodo repository: works with GitHub
http://zenodo.org

# MINE: mutual information NonParametric Data Exploration - R package to C++ functions - mostly bio
http://www.exploredata.net/
http://www.exploredata.net/Technical-information
http://www.eecs.harvard.edu/~michaelm/
http://www.mybiasedcoin.blogspot.com/
http://www.sabetilab.org/
http://intelligenttradingtech.blogspot.com/2012/01/there-was-lot-of-buzz-in-blogosphere-as.html


#########
### numerical methods dimension reduction

# Juha Karhunen - Independent Component Analysis
http://users.ics.aalto.fi/juha/
# Independent Component Analysis (ICA)
http://www.cis.hut.fi/Opinnot/T-61.6030/k2006/
http://cis.legacy.ics.tkk.fi/aapo/papers/IJCNN99_tutorialweb/
http://research.ics.aalto.fi/ica/icademo/

# David S. Matteson R package steadyICA Dimension Reduction for Multivariate Time Series
https://davidsmatteson.com/
http://ec2-52-90-141-138.compute-1.amazonaws.com:3838/

# Bryan W. Lewis at SciDB - package irlba for SVD and PCA
http://illposed.net/
http://bwlewis.github.io/GLM
http://bwlewis.github.io/cassini/



#########
### lectures and tutorials

# Learn How to Code - Where to start
http://gettinggeneticsdone.blogspot.com/2012/01/new-years-resolution-learn-how-to-code.html

# Tutorials
http://www.tutorialspoint.com/index.htm
http://www.tutorialspoint.com/shorttutorials/run-your-first-program-in-mingw-compiler

# Vivian Zhang
http://nycdatascience.com/

# Code School: Ruby, Java, and web technologies
https://www.codeschool.com/



#########
### languages and systems

# JSON is a replacement for XML, YAML is a superset of JSON
https://en.wikipedia.org/wiki/JSON



#########
### julia language for technical computing

http://julialang.org/
http://strata.oreilly.com/2012/10/matlab-r-julia-languages-for-data-analysis.html

# JuliaQuant Quantitative Finance in Julia
https://github.com/JuliaQuant


### Church probabilistic programming language 
http://projects.csail.mit.edu/church/wiki/Church

# Probabilistic Models of Cognition by Noah D. Goodman
https://probmods.org/index.html
http://web.stanford.edu/~ngoodman/

# regex regular expression tutorial
http://www.coppelia.io/quick-start-regex-for-analysts-part-i/
http://www.coppelia.io/category/regex/



#########
### version control

# version control
http://stackoverflow.com/questions/406038/subversion-usages/406176#406176
# mercurial - modern, open source, distributed version control system
http://hginit.com/
# great review of Distributed Version Control Systems: GIT killer feature git-gisect
http://www.infoq.com/articles/dvcs-guide

# Source Tree version control
http://www.sourcetreeapp.com/



#########
### Git version control

### Git Home
http://git-scm.com/
http://en.wikipedia.org/wiki/Git_(software)
http://en.wikipedia.org/wiki/GitHub

### Git learn

# Git beginner guides
http://gitimmersion.com/
http://backlogtool.com/git-guide/en/

# Git reference
http://git-scm.com/docs
# Git quick reference
http://gitref.org/
# Pro Git book
http://git-scm.com/book/en/v2

# Learn Git command line
http://stackoverflow.com/questions/315911/git-for-beginners-the-definitive-practical-guide
http://try.github.io/levels/1/challenges/1
http://stackoverflow.com/questions/457927/git-workflow-and-rebase-vs-merge-questions

# GitHub Video Guides
http://www.youtube.com/GitHubGuides

# Git tutorial
http://git-scm.com/book/en/Getting-Started-First-Time-Git-Setup
http://git-scm.com/book/en/Getting-Started-Git-Basics
http://git-scm.com/book/en/Git-Basics-Tips-and-Tricks
http://www.codeproject.com/Articles/457305/Basic-Git-Command-Line-Reference-for-Windows-Users

# Configuring Git over SSH to login once and for all
http://applyr.blogspot.com/2014/05/rstudio-pushing-to-github-with-ssh.html
http://stackoverflow.com/questions/1595848/configuring-git-over-ssh-to-login-once
git config --global user.name "algoquant"
git config --global user.email "algoquant@algoquants.ch"
git config remote.origin.url git@github.com:algoquant/HighFreq.git

# Excluding files
http://git-scm.com/docs/gitignore

# R Git Version control
http://stackoverflow.com/questions/15547555/create-an-offline-version-control-for-r-studio
http://stackoverflow.com/questions/15547555/create-an-offline-version-control-for-r-studio
http://stackoverflow.com/questions/2712421/r-and-version-control-for-the-solo-data-analyst

# unix bash ref
http://ss64.com/bash/



#########
### GitHub

# my GitHub files
https://github.com/algoquant/

# duplicating a repository
https://help.github.com/articles/duplicating-a-repository/
http://stackoverflow.com/questions/10963878/how-do-you-fork-your-own-project-on-github

# GitHub create new organization and transfer repositories
https://help.github.com/articles/creating-a-new-organization-from-scratch/
https://help.github.com/articles/transferring-a-repository/
https://github.com/pricing

# GitHub scraping repo recommendations
http://gitrec.mortardata.com/

# BFG Repo-Cleaner
http://robinlovelace.net/r/2014/06/25/pruning-a-giant-gh-repo.html
http://rtyley.github.io/bfg-repo-cleaner/
https://github.com/rtyley/bfg-repo-cleaner

# travis ci unit test for GitHub projects
https://travis-ci.org/profile/algoquant

# githut statistics on computer languages in github
http://githut.info/

### GitHub learning

# GitHub tutorials
https://plot.ly/r/github-getting-started-for-data-scientists/
https://help.github.com/categories/bootcamp/
https://help.github.com/articles/good-resources-for-learning-git-and-github/
http://kbroman.org/github_tutorial/
# tip: use GitHub Desktop instead of using git command line
https://desktop.github.com/
# fork, pull, & merge
https://help.github.com/articles/be-social
https://help.github.com/articles/using-pull-requests

# GitHub support blog by Phil Haack
http://haacked.com/
# Writing on GitHub
https://help.github.com/articles/writing-on-github
# how-to gist
https://help.github.com/articles/creating-gists
# GitHub Report Card - dynamically generated contributions report
http://osrc.dfm.io/

### Interactive books using GitHub/Git and markdown
https://www.gitbook.com/

https://www.gitbook.com/book/snowdream86/github-cheat-sheet/details

http://jason.bryer.org/posts/2014-04-18/Gitbook_with_R_Markdown.html
# Publishing in GitHub
http://sumanmathmedicine.blogspot.com/2014/04/publishing-in-github.html

# GitHub wiki
https://github.com/blog/1828-wikis-now-with-more-love
https://github.com/showcases/projects-with-great-wikis

# ZenHub
https://www.zenhub.io/
https://www.zenhub.io/beta
https://github.com/axiomzen/zenhub-now
# Ruby app that analyzes GitHub Issue Comments, Milestones, and Code Commit Messages for Time Tracking and Budget Tracking information.
https://github.com/StephenOTT/GitHub-Time-Tracking



#########
### GitHub Pages

### GitHub hosted web page
http://algoquant.github.io/

# GitHub hosted web page setup how-to
https://pages.github.com/

# GitHub Pages
https://github.com/blog/272-github-pages
https://help.github.com/articles/setting-up-a-custom-domain-with-pages
https://help.github.com/articles/user-organization-and-project-pages
https://help.github.com/categories/20/articles
# step by step guide
http://www.thinkful.com/learn/a-guide-to-using-github-pages/
http://webdesign.tutsplus.com/tutorials/getting-to-know-github-pages-static-project-pages-fast--webdesign-11501
# create blog on github pages using jekyll
http://joshualande.com/jekyll-github-pages-poole/

# Jekyll
https://jekyllrb.com/
https://talk.jekyllrb.com/

# website modified from Skeleton, hosted on Github Pages using Jekyll
http://jason.bryer.org/about.html
# Using Rmarkdown, Jekyll, & GitHub for a Website
http://jason.bryer.org/posts/2012-12-10/Markdown_Jekyll_R_for_Blogging.html
http://www.getskeleton.com/
# another website
http://seananderson.ca/colophon.html
https://github.com/seananderson/seananderson.github.com
# nice website
https://github.com/jbkunst/jbkunst.github.io/tree/master/_posts


#########
### Travis CI

# Travis CI is a continuous integration service used to build and test software projects hosted at GitHub
https://travis-ci.org/



###########
### publishing presentations documents markdown rmarkdown CSS LaTeX

### markdown

# learning markdown tutorials
https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet
http://daringfireball.net/projects/markdown/syntax
https://guides.github.com/features/mastering-markdown/

# markdown cheatsheet
https://markdown-it.github.io/
https://daringfireball.net/projects/markdown/
https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet

# markdown interpreter
http://www.markitdown.net/markdown

# GitHub Flavored Markdown
https://help.github.com/articles/github-flavored-markdown/
# github flavored markdown
https://help.github.com/categories/writing-on-github/
http://github.github.com/github-flavored-markdown/sample_content.html


### Power BI Desktop
https://powerbi.microsoft.com/en-us/desktop/
http://blog.revolutionanalytics.com/2015/11/powerbi-adds-support-for-r.html
https://www.simple-talk.com/sql/bi/using-r-machine-learning-script-as-a-power-bi-desktop-data-source/


### R Markdown

# RMarkdown v2
http://rmarkdown.rstudio.com/
https://guides.github.com/features/mastering-markdown/
http://scholarlymarkdown.com/Scholarly-Markdown-Guide.html
http://blog.rstudio.org/2014/06/18/r-markdown-v2/
http://rmarkdown.rstudio.com/authoring_migrating_from_v1.html

# rmarkdown templates
https://rud.is/b/2016/02/04/alternate-r-markdown-templates/
https://github.com/hrbrmstr/markdowntemplates
http://svmiller.com/blog/2016/02/svm-r-markdown-manuscript/

# markdown tutorials
http://gastonsanchez.com/blog/how-to/2012/11/19/Creating-HTML5-slides.html
https://rpubs.com/marschmi/RMarkdown
# markdown showcase
http://yihui.name/knitr/demo/showcase/
http://cpsievert.github.io/slides/markdown/

# markdown plus plotly tutorial
http://moderndata.plot.ly/r-markdown-and-knitr-tutorial-part-1/
http://moderndata.plot.ly/r-markdown-and-knitr-tutorial-part-2/

# publishing R Markdown on GitHub Pages
https://plot.ly/r/github-getting-started-for-data-scientists/
http://happygitwithr.com/
https://github.com/jennybc/happy-git-with-r
http://user2016.org/tutorials/01.html

# Customizing Markdown Rendering
https://support.rstudio.com/hc/en-us/articles/200552186-Customizing-Markdown-Rendering

# rmarkdown tutorial
https://rpubs.com/moviedo/Rmd_tutorial

# comments in markdown
http://stackoverflow.com/questions/4823468/store-comments-in-markdown-syntax

# YAML today's date
http://stackoverflow.com/questions/23449319/yaml-current-date-in-rmarkdown
# YAML authors
http://stackoverflow.com/questions/26043807/multiple-authors-and-subtitles-in-rmarkdown-yaml
http://stackoverflow.com/questions/29532214/add-author-affiliation-in-r-markdown-beamer-presentation/29532605

# markdown with CSS
http://rstudio-pubs-static.s3.amazonaws.com/27777_55697c3a476640caa0ad2099fe914ae5.html
# markdown import image scale
http://stackoverflow.com/questions/14675913/how-to-change-image-size-markdown

http://www.codinghorror.com/blog/2012/10/the-future-of-markdown.html
https://onlinecourses.science.psu.edu/statprogram/markdown
http://cran.r-project.org/web/packages/knitr/vignettes/knitr-markdown.html
http://stackoverflow.com/questions/17963962/plot-size-and-resolution-with-r-markdown-knitr-pandoc-beamer
http://cran.r-project.org/web/packages/knitr/vignettes/knitr-markdown.html
http://jeromyanglim.blogspot.com/2012/05/getting-started-with-r-markdown-knitr.html
http://kbroman.org/knitr_knutshell/pages/Rmarkdown.html
https://rpubs.com/Thell/knitLiteralMarkdownPreview

# rticles package with R markdown article templates
https://github.com/rstudio/rticles

# presentation color schemes
http://blog.slideshare.net/2014/03/17/how-to-use-colors-in-presentations/
https://kuler.adobe.com/create/color-wheel/
http://colorschemedesigner.com/

# Convert Sweave LaTeX to knitr R Markdown
http://jeromyanglim.blogspot.com/2012/06/how-to-convert-sweave-latex-to-knitr-r.html


### rpres - old format replacred by R Markdown

https://support.rstudio.com/hc/en-us/sections/200130218-R-Presentations
https://support.rstudio.com/hc/en-us/articles/200486468-Authoring-R-Presentations


### CSS

# CSS learn
http://www.w3schools.com/default.asp
http://www.w3schools.com/css/default.asp
https://www.codecademy.com/tracks/web

# CSS for shiny app
http://shiny.rstudio.com/articles/css.html

# CSS examples templates
http://cssdeck.com/

# CSS length units
http://www.w3.org/Style/Examples/007/units.en.html

# CSS colors
http://www.w3schools.com/html/html_colornames.asp
http://www.w3schools.com/html/html_colorvalues.asp
http://www.w3schools.com/html/html_colors.asp

# hover effect
http://cssdeck.com/labs/pzam7ug7
http://www.w3schools.com/cssref/sel_hover.asp

# Bootstrap tools for creating web applications containing HTML, CSS, and JavaScript
https://en.wikipedia.org/wiki/Bootstrap_(front-end_framework)

### R Markdown slide presentations

# good introduction: difference between ioslides presentations versus slidy
http://data-analytics.net/cep/Schedule_files/presentations_demo.html

# slides with two two columns
http://stackoverflow.com/questions/20847371/two-column-layouts-in-rstudio-presentations-slidify-pandoc
http://stackoverflow.com/questions/31753897/2-column-section-in-r-markdown
http://stackoverflow.com/questions/30514408/two-columns-code-in-markdown
http://stackoverflow.com/questions/19237190/2-column-report-in-r-markdown-render-html-aside-data-frame
http://stackoverflow.com/questions/28201503/how-to-render-multiple-columns-with-markdown-in-github-readme

http://rmarkdown.rstudio.com/ioslides_presentation_format.html#advanced-layout



### HTML

# HTML tutorial
http://www.w3schools.com/html/html_intro.asp
http://www.w3schools.com/html/default.asp

# HTML CSS tutorial
https://www.codecademy.com/tracks/web

# R2HTML package - not needed if using knitr and .Rmd
http://www.statmethods.net/interface/output.html


### simple websites

http://getskeleton.com/
https://github.com/dhg/Skeleton


### LaTeX Wiki
http://en.wikibooks.org/wiki/LaTeX/
http://en.wikibooks.org/wiki/LaTeX/Mathematics#Greek_letters
http://en.wikibooks.org/wiki/LaTeX/Mathematics#List_of_Mathematical_Symbols
http://en.wikibooks.org/wiki/LaTeX/Fonts
http://en.wikibooks.org/wiki/LaTeX/Presentations
http://en.wikibooks.org/wiki/LaTeX/Hyperlinks

# LaTeX learn
https://www.sharelatex.com/learn/

# LaTeX Science equations - math formulas
http://en.wikibooks.org/wiki/LaTeX/Mathematics
http://vgoulet.act.ulaval.ca/en/latex/
http://vgoulet.act.ulaval.ca/pub/tables/table_t.Rnw
http://equplus.net/
# LaTeX amsmath aligning equations
https://www.sharelatex.com/learn/Aligning_equations_with_amsmath
http://tex.stackexchange.com/questions/59214/left-align-math

# LaTeX code from hand-written symbol using character recognition
http://detexify.kirelabs.org/classify.html
http://shapecatcher.com/

# bbold package for unitary vector or matrix symbol
# \usepackage{bbold}

# MathJax equations
http://meta.math.stackexchange.com/questions/5020/mathjax-basic-tutorial-and-quick-reference



# managing temp and aux files
http://tex.stackexchange.com/questions/11123/prevent-pdflatex-from-writing-a-bunch-of-files/11130#11130
# automating document compilation
http://tex.stackexchange.com/questions/64/tools-for-automating-document-compilation/77879#77879
http://tex.stackexchange.com/questions/24785/deleting-external-auxiliary-files

# LaTex online editors
https://www.sharelatex.com/
https://www.writelatex.com/

# Biblatex
https://www.sharelatex.com/blog/2013/07/31/getting-started-with-biblatex.html
http://www.sharelatex.com/learn/Bibliography_management
http://www.bibtex.org/Using/
http://tex.stackexchange.com/questions/13509/biblatex-for-idiots
http://shelah.logic.at/eindex.html
http://tex.stackexchange.com/questions/68080/beamer-bibliography-icon
# Biber with Biblatex
http://tex.stackexchange.com/questions/44040/biblatex-biber-texmaker-miktex
http://tex.stackexchange.com/questions/69705/how-to-automate-using-biber-in-miktex-texworks
http://tex.stackexchange.com/questions/66201/how-do-i-set-up-my-latex-document-to-enable-me-to-cite-from-my-bibtex-bibliograp

# LaTeX for Beginners
http://math.ucr.edu/~huerta/latexforbeginners.html
# Highlight syntax highlighter for LaTeX, TeX, HTML, XHTML, RTF
http://www.andre-simon.de/doku/highlight/en/highlight.php

# LaTeX escape special characters/symbols
http://www.cespedes.org/blog/85/how-to-escape-latex-special-characters
To get	You must use
#	\#
$	\$
%	\%
&	\&
\	\textbackslash{}
^	\textasciicircum{}
_	\_
{	\{
}	\}
~	\textasciitilde{}

# LaTeX source code listing like in professional books
http://stackoverflow.com/questions/741985/latex-source-code-listing-like-in-professional-books

# Latex two columns
http://texblog.org/2007/08/11/creating-two-columns-in-article-report-or-book/

# LaTeX tips and tricks
http://www.f.kth.se/~ante/latex.php

# LaTeX setup for Windows
http://www.latexbuch.de/install-latex-windows-7/

# Extensive support for hypertext in LaTeX
http://ctan.org/pkg/hyperref

### Beamer
# Beamer is a LaTeX document class for creating slides for presentations.
http://en.wikipedia.org/wiki/Beamer_(LaTeX)
# LaTeX Beamer quick tutorial
http://heather.cs.ucdavis.edu/~matloff/beamer.html

http://www.math-linux.com/latex-26/How-to-make-a-presentation-with

# Beamer tips - table, appendix, multiline equations
http://everythingyouforgetaboutlatex.blogspot.com/

# Beamer bugs errors
# add \protect to \emph{class}
http://tex.stackexchange.com/questions/31612/using-textit-in-section-with-beamer-class
http://tex.stackexchange.com/questions/47301/error-with-bold-font-in-section-title-with-beamer?lq=1

# Shifting Figure to the Left in Beamer
http://tex.stackexchange.com/questions/44322/shifting-figure-to-the-left-in-beamer

http://people.sc.fsu.edu/~jburkardt/latex/beamer_example/beamer_example.html

# ctan.org has massive archive of beamer files
http://www.ctan.org/pkg/beamer
http://www.ctan.org/tex-archive/macros/latex/contrib/beamer/

# Top-aligning columns within a top-aligned frame - still doesn't work
http://tex.stackexchange.com/questions/16447/beamer-top-aligning-columns-within-a-top-aligned-frame

http://www.math.umbc.edu/~rouben/beamer/quickstart-Z-H-3.html
http://www.math.umbc.edu/~rouben/beamer/quickstart-Z-H-8.html#node_sec_8
http://www.math.umbc.edu/~rouben/beamer/quickstart-Z-H-5.html#node_sec_5
http://genomeek.wordpress.com/2013/06/13/beamer-theme-creation/
http://programming-r-pro-bro.blogspot.com/2011/11/create-your-own-beamer-template.html
http://tex.stackexchange.com/questions/102791/simple-beamer-template-for-total-beginners
http://mathblog.zeronteproject.com/76/

# Simple beamer slides
http://kbroman.wordpress.com/2013/10/07/better-looking-latexbeamer-slides
http://www.latextemplates.com/template/beamer-presentation
https://www.writelatex.com/622077ncschz#/1285465
http://faculty.lagcc.cuny.edu/tnagano/research/presentationWithLaTeX/index.htm

# Adding logo or image in title slide
http://tex.stackexchange.com/questions/21356/adding-custom-image-in-title-slide
http://tex.stackexchange.com/questions/21165/customization-of-the-title-slide-in-a-beamer-presentation
http://tex.stackexchange.com/questions/13912/how-to-insert-an-image-in-the-front-cover-of-a-report
http://tex.stackexchange.com/questions/194449/author-and-institute-dont-appear-when-adding-background-image-to-title-page-in
http://tex.stackexchange.com/questions/7916/how-to-insert-a-background-image-in-a-beamer-frame

# Custom beamer blocks
http://tex.stackexchange.com/questions/28760/custom-beamer-blocks-for-pros-and-cons
http://tex.stackexchange.com/questions/117380/beamer-how-to-make-theorem-like-block-with-free-title

# Gallery of beamer themes
http://www.hartwork.org/beamer-theme-matrix
http://deic.uab.es/~iblanes/beamer_gallery/index_by_theme.html

# beamer title email field: hyperref warning "token not allowed in a PDF string"
http://tex.stackexchange.com/questions/10555/hyperref-warning-token-not-allowed-in-a-pdf-string



### Sweave
# The Sweave Homepage
http://www.stat.uni-muenchen.de/~leisch/Sweave/

# Installing Sweave for Miktex 2.9 on Windows 7
http://tex.stackexchange.com/questions/117621/installing-sweave-for-miktex-2-9-on-windows-7

# Sweave Demo
http://users.stat.umn.edu/~geyer/Sweave/

http://www.rstudio.com/ide/docs/authoring/overview
http://www.rstudio.com/ide/docs/authoring/rnw_weave


### knitr package
http://cran.r-project.org/web/packages/knitr/index.html
# knitr FAQ
https://github.com/yihui/knitr/blob/master/FAQ.md
# Yihui Xie developed knitr
http://yihui.name/en/
http://yihui.name/knitr/
http://yihui.name/knitr/options/
http://yihui.name/knitr/demo/output
http://yihui.name/knitr/demos/
http://yihui.name/knitr/demo/sweave/
http://yihui.name/knitr/demo/editors/
http://yihui.name/knitr/demo/minimal/
http://yihui.name/knitr/demo/showcase/
https://github.com/yihui/
https://github.com/yihui/knitr/
https://github.com/yihui/knitr/tree/master/inst/examples/
# debugging code chunk
http://stackoverflow.com/questions/19286051/some-plots-not-rendering-in-rstudio-knitr-rmarkdown

# knitr learning
http://kbroman.org/knitr_knutshell/

# Text and plot in two columns
http://tex.stackexchange.com/questions/83361/two-column-layout-with-text-and-knitr-chunk
# knitr chunk options to control line spacing, font size in output
http://stackoverflow.com/questions/20097553/knitr-chunk-options-to-control-line-spacing-font-size-in-output
# R code output width
https://github.com/yihui/knitr-examples/blob/master/038-output-width.Rnw
http://stackoverflow.com/questions/13428007/r-chunk-code-stay-inside-the-beamer-frame

# knitr and listings package
http://yihui.name/knitr/demo/listings/
https://github.com/yihui/knitr/blob/master/inst/examples/knitr-listings.Rnw
http://tex.stackexchange.com/questions/100088/r-code-linebreaks-and-code-highlighting-in-knitr

# Color source code blocks
http://stackoverflow.com/questions/14695892/change-code-block-color-in-knitr-markdown

# extract R chunks into .R file
library(knitr)
purl('C:/Develop/R/lecture_slides/FRE6871_Lecture_1.Rnw', documentation=0)

# knitr example
https://github.com/lcolladotor/lcollado753/blob/master/template_short_commented_knitr.Rnw

# knitr and xtable example
http://stackoverflow.com/questions/21397407/knitr-r-markdown-and-xtable-xtable-tables-within-html-table

http://scg.sdsu.edu/wp-content/uploads/2014/04/r_and_sweave.pdf

# knitr animation plots in output html and pdf (install ffmpeg first)
http://stackoverflow.com/questions/14879210/including-a-interactive-3d-figure-with-knitr
https://github.com/yihui/knitr/issues/952
http://stackoverflow.com/questions/25202887/animated-rgl-graphs-with-knitr
http://stackoverflow.com/questions/12038893/combining-r-markdown-and-animation-package
http://stackoverflow.com/questions/31187561/animation-in-pdfs-using-knitr-that-supports-more-than-just-adobe-reader
http://stackoverflow.com/questions/28078104/speed-up-fig-show-interval-in-knitr

http://www.rstudio.com/ide/docs/authoring/overview
http://www.rstudio.com/ide/docs/authoring/rnw_weave
http://www.numbertheory.nl/2012/02/12/r-and-presentations-a-basic-example-of-knitr-and-beamer
http://danieljhocking.wordpress.com/2013/09/25/knitting-beautiful-documents-in-rstudio

# Create PowerPoint with R, Knitr, Pandoc, and Slidy
http://rprogramming.net/create-a-slideshow-powerpoint-with-r-knitr-pandoc-and-slidy/
# Create HTML or PDF files from .Rmd files with R, Knitr, MiKTeX, and Pandoc
http://rprogramming.net/create-html-or-pdf-files-with-r-knitr-miktex-and-pandoc/

# Christopher Gandrud - Reproducible Research at RStudio
https://github.com/christophergandrud/Rep-Res-Book
http://christophergandrud.blogspot.com/

# Lyx
http://www.lyx.org/Features
http://stackoverflow.com/questions/17891108/lyx-knitr-error
http://yihui.name/en/2013/02/contribute-to-the-r-journal-with-lyx-knitr/
http://yihui.name/knitr/demo/lyx/


### Python sweave2knitr
https://pypi.python.org/pypi/Sweave2knitr/0.1.4
https://github.com/dgrtwo/Sweave2knitr

# R and My Divorce From Word
http://mbjoseph.github.io/blog/2013/10/30/word/
https://github.com/mbjoseph/project-template


# Introduction to knitr - R Markdown (Rmd) format
http://lcolladotor.github.io/Rmd-intro/#1

# RPubs - RStudio R Markdown documents
http://rpubs.com/

https://public.opencpu.org/
http://blog.revolutionanalytics.com/2012/08/creating-beautiful-reports-from-r-with-knitr.html
http://recology.info/2013/07/r-resources/
http://jeromyanglim.blogspot.com/2012/06/converting-sweave-latex-to-knitr-latex.html
http://www.latextemplates.com/template/beamer-presentation
https://sites.google.com/site/siamlatextutorial/tutorials/slideshows

# Deleting external auxiliary files
http://support.rstudio.org/help/discussions/questions/902-aux-file-and-tex-file-produced-by-r
http://tex.stackexchange.com/questions/24785/deleting-external-auxiliary-files
http://tex.stackexchange.com/questions/87816/is-there-a-way-to-configure-pdflatex-to-make-all-but-the-tex-and-pdf-files-hid



###########
### slidify

# slidify package reproducible html5 slides from R markdown - Ramnath Vaidyanathan
http://ramnathv.github.io/slidify/
https://github.com/slidify
http://slidify.github.io/workshops/
http://slidify.org/
# UseR tutorial 2014
http://ramnathv.github.io/user2014-idocs-slides/
http://ramnathv.github.io/user2014-idocs/
https://github.com/ramnathv/user2014-idocs/stargazers

# nice slidify with source: Grammar of (Interactive) Graphics
http://mattsigal.github.io/InteractiveGraphics/#1
https://github.com/mattsigal/InteractiveGraphics
# Matthew Sigal psychology and data visualization
http://www.matthewsigal.com/
https://github.com/mattsigal/
# nice ggplot
http://rpubs.com/mattsigal/

# examples
http://www.magesblog.com/2013/05/interactive-presentation-with-slidify.html
http://blog.revolutionanalytics.com/2012/11/create-elegant-interactive-presentations-from-r-with-slidify.html


#########
### Google Apps

# Sign in to the Google Apps
http://admin.google.com/
Twinkle14

# Google Apps domain setup
http://www.google.com/intx/en/enterprise/apps/business/pricing.html
http://lifehacker.com/5391537/trick-out-google-apps-for-your-domain
# Google Apps domain setup
http://www.leemunroe.com/io-domains-heroku/

# Google Apps setup
https://admin.google.com/algoquants.ch/AdminHome#SetupWizard
http://deployment.googleapps.com/
http://deployment.googleapps.com/Home/enterprise-business
# Google Apps Help Center
https://support.google.com/a/?hl=en#topic=29157
http://learn.googleapps.com/
https://support.google.com/a/answer/53340?hl=en

https://docs.google.com/a/algoquants.ch/document/d/1yn-eiyCaecx7GdPQYCG4g9UcHbvoNJuUuUWX09Yt1z4/edit

# Gmail address:
http://mail.google.com/a/algoquants.ch
# Google Drive address:
http://drive.google.com/a/algoquants.ch

# Google Sites website
https://sites.google.com/
http://sites.google.com/a/algoquants.ch
# Google Sites overview
http://learn.googleapps.com/sites
https://support.google.com/a/answer/90915

Create your website for algoquants.ch 
You may start to build your new website for your domain simply by visiting: 
www.algoquants.ch

https://admin.google.com/algoquants.ch/AdminHome#SetupWizard/setuptask=CREATE_SITE_SHARE_SUBTASK

https://admin.google.com/algoquants.ch/AdminHome#ServiceSettings/service=sites&subtab=address

https://admin.google.com/algoquants.ch/AdminHome#ServiceSettings/notab=1&service=sites&subtab=address

www.algoquants.ch points to the site "home" which does not exist. Please contact the domain administrator.

# Troubleshoot CNAME records - domain DNS settings
https://support.google.com/a/answer/93595
nslookup -q=cname www.algoquants.ch
nslookup www.algoquants.ch


https://admin.google.com/algoquants.ch/AdminHome?chromeless=1#OGX:DomainSettingsAdvancedDns

https://www.domaindiscount24.com/en
Sign-in name : 03796724
Password : rulaKYJPJins

Domaindiscount24.com customer service email : support@domaindiscount24.com

# blogger create a new page on your blog
https://support.google.com/blogger/answer/165955?hl=en

# Google Drive
http://stackoverflow.com/questions/13328247/how-to-copy-a-file-into-a-specific-folder-using-docslist
http://webapps.stackexchange.com/questions/40164/can-i-duplicate-a-folder-in-google-drive
http://stackoverflow.com/questions/14696514/how-do-i-copy-move-a-file-to-a-folder-in-google-apps-script



#########
### hosting

# hosting Ruby, Node.js, Python, Java, and PHP
https://www.heroku.com/

# hosting data projects Mongo Hadoop
http://www.mortardata.com/
http://blog.mortardata.com/

http://www.r-bloggers.com/instructions-for-installing-using-r-on-amazon-ec2/
http://www.r-bloggers.com/how-to-run-r-in-the-cloud-for-teaching/
# Google App Engine
https://cloud.google.com/products/app-engine

http://www.infoworld.com/d/cloud-computing/amazon-web-service-now-offers-r-demand-236079

# Diff and merge tools
kdiff3, tkdiff, meld, xxdiff, emerge, vimdiff, gvimdiff, ecmerge, opendiff


https://wiki.archlinux.org/index.php/Gitweb

http://wiki.dreamhost.com/Main_Page

http://sourceforge.net/
https://r-forge.r-project.org/

http://code.google.com/

https://developers.google.com/

http://en.wikipedia.org/wiki/Comparison_of_open_source_software_hosting_facilities



#########
### publishing

# Discover, curate and publish content online
http://www.scoop.it/

# online newspaper publishing
http://paper.li

# Pandoc universal document converter
http://johnmacfarlane.net/pandoc/
https://github.com/jgm/pandoc

### publishing Gists

# Mike Bostock’s blocks for publishing html and Java Gists - very cool!
http://bl.ocks.org/mbostock

# publish Markdown Gists - a little more visually appealing, but can't control buttons
# roughdraft is better
http://www.roughdraft.io/
http://gist.io/

# GistBox good GUI for editing Gists - also create bookmarks as individual Gists
http://www.gistboxapp.com/
http://blog.gistboxapp.com/post/47278471382/gists-are-bookmarks-with-context

# use Google+ community for creating and sharing bookmarks
http://www.techrepublic.com/blog/google-in-the-enterprise/save-and-share-bookmarks-with-google/
http://socialmediaslant.com/bookmark-google-plus-posts-extension-chrome/

# create and share bookmarks
https://delicious.com/

# create and share notebooks and bookmarks
https://www.evernote.com/
https://discussion.evernote.com/topic/27201-using-evernote-as-a-bookmark-manager/

# Dexy tool for writing documents incorporating code
# Dexy is a very similar product to Sweave or knitR
# Dexy is used by opengamma.org
http://dexy.it/
# Ana Nelson
http://ananelson.com/
http://dexy.it/docs/what-is-dexy.html
http://stackoverflow.com/search?q=dexy

# online HTML/CSS/JS Devs interpreter
http://cssdeck.com/

### scraping news and data
http://blog.hartleybrody.com/web-scraping/
https://scraperwiki.com/
http://datajournalismhandbook.org/1.0/en/getting_data_3.html
http://www.outwit.com/products/hub/download/?gclid=CM2XxIWumL4CFcNlOgodgQwAvg#ectrans=1
http://www.adbnews.com/

# Beautiful Soup Python news scraping
https://wiki.python.org/moin/beautiful%20soup
https://pypi.python.org/pypi/beautifulsoup4/4.3.2
https://github.com/zeber19/headlines/blob/master/headline_scrape.py


### Style Guides
# Google Style Guide
https://code.google.com/p/google-styleguide/
# Making Wrong Code Look Wrong
http://www.joelonsoftware.com/articles/Wrong.html

# LaTex online editors
https://www.sharelatex.com/
https://www.writelatex.com/



#########
### visualization

# beautiful visualizations
http://venturebeat.com/2014/08/09/data-is-beautiful-is-a-hidden-gem-for-gorgeous-data-visualizations/
http://www.reddit.com/r/dataisbeautiful/


### D3.js and Protovis

# D3.js - Data-Driven Documents
http://d3js.org/
http://vis.stanford.edu/papers/d3
http://en.wikipedia.org/wiki/D3.js

# Mike Bostock, developer of D3.js, works for New York Times
http://bost.ocks.org/mike/
https://github.com/mbostock/d3
# D3 gallery
https://github.com/mbostock/d3/wiki/Gallery

# Visualizing Algorithms
http://bost.ocks.org/mike/algorithms/
https://github.com/mbostock

# NVD3 Re-usable finance charts for d3.js
http://nvd3.org/
https://github.com/novus/nvd3

# interactive graphs Python, d3.js, R, shiny, IPython, vincent, d3py, python-nvd3
http://www.xavierdupre.fr/blog/2013-11-30_nojs.html

# Rob Story: vincent - Python to Vega translator
http://wrobstory.github.io/
https://github.com/wrobstory
# Data in Python with Pandas and Vincent
http://wrobstory.github.io/2013/10/mapping-data-python.html
# Quandl and Pandas
http://wrobstory.github.io/2013/04/quandl-pandas.html
# Vincent and iPython
http://wrobstory.github.io/2013/04/vincent-ipython.html
# Plotting Time Series with Pandas DatetimeIndex and Vincent
http://wrobstory.github.io/2013/04/pandas-vincent-timeseries.html

# Junk Charts by Kaiser Fung
http://junkcharts.typepad.com/

# Chrome Experiments showcase of HTML5 and JavaScript
http://www.chromeexperiments.com/

# plotly: create interactive charts with ggplot2 syntax
https://plot.ly/
https://plot.ly/r/
https://github.com/plotly

# slideshow: Grammar of (Interactive) Graphics
http://mattsigal.github.io/InteractiveGraphics/#1

# Social Network Analysis visualization
https://gephi.org/
http://www.vesnam.com/Rblog/viznets2/
https://github.com/mbastian

http://damienmortini.me.uk/
https://github.com/dmmn

# WebGL
https://github.com/vvuk
http://www.chromeexperiments.com/webgl/
http://jaxry.github.io/colorful-life/

# Kevin Quealy’s graphics editor at The New York Times
https://kpq.github.io/chartsnthings/
https://twitter.com/KevinQ

# TradingView HTML5 visualization in browser for collaborating on trade ideas
# TradingView supplies the data
https://www.tradingview.com/
http://blog.tradingview.com/



#########
### Project Management

# Semantic Versioning
http://semver.org/

http://machinelearningmastery.com/how-to-layout-and-manage-your-machine-learning-project/

# GitHub project management tools
http://www.quora.com/Which-project-management-tools-are-deeply-integrated-with-GitHub
http://www.thesambarnes.com/web-project-management/web-project-management-and-collaboration-using-github/
http://www.lullabot.com/blog/article/managing-projects-github

# looks interesting
https://huboard.com/
https://www.zenhub.io/
https://taskk.it/github

# reproducible data science using Python
http://datascience.stackexchange.com/questions/758/tools-and-protocol-for-reproducible-data-science-using-python/759#759



#########
### misc utilities

# compare two texts
https://www.diffchecker.com/
http://www.text-compare.com/



#########
### windows

# set the path and environment variables in Windows
http://www.computerhope.com/issues/ch000549.htm
http://msdn.microsoft.com/en-gb/library/windows/desktop/ms682653.aspx

# got to 
Control Panel\System and Security\System
click Advanced System Settings
click Environment Variables button under Advanced tab
edit PATH
C:\Program Files (x86)\NVIDIA Corporation\PhysX\Common;C:\Program Files (x86)\Intel\iCLS Client\;C:\Program Files\Intel\iCLS Client\;C:\windows\system32;C:\windows;C:\windows\System32\Wbem;C:\windows\System32\WindowsPowerShell\v1.0\;C:\Program Files (x86)\Intel\OpenCL SDK\2.0\bin\x86;C:\Program Files (x86)\Intel\OpenCL SDK\2.0\bin\x64;C:\Program Files\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files\Intel\Intel(R) Management Engine Components\IPT;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\DAL;C:\Program Files (x86)\Intel\Intel(R) Management Engine Components\IPT;C:\Program Files\Lenovo\Bluetooth Software\;C:\Program Files\Lenovo\Bluetooth Software\syswow64;C:\Program Files\MiKTeX 2.9\miktex\bin\x64\;%SystemRoot%\system32;%SystemRoot%;%SystemRoot%\System32\Wbem;%SYSTEMROOT%\System32\WindowsPowerShell\v1.0\

# Add/Edit Environment Variables in Windows 7
http://www.nextofwindows.com/how-to-addedit-environment-variables-in-windows-7/

# recovering PATH value
http://stackoverflow.com/questions/4477660/what-is-the-difference-between-user-variables-and-system-variables



#########
### other

# MinGW
http://nuwen.net/mingw.html
http://mingw.org/wiki/HOWTO
http://www.mingw.org/wiki/Getting_Started
http://www.mingw.org/wiki/MinGW_for_First_Time_Users_HOWTO
http://www.mingw.org/wiki/InstallationHOWTOforMinGW
http://www.mingw.org/wiki/HOWTO_Install_the_MinGW_GCC_Compiler_Suite

http://stackoverflow.com/questions/14430944/i-cant-find-my-mingw-shell-after-installing-with-gui-installer
http://stackoverflow.com/questions/13089383/setting-up-mingw-in-windows-7-to-compile-adequately-portable-c-code
http://stackoverflow.com/questions/6394755/how-to-install-gcc-on-windows-7-machine
http://stackoverflow.com/questions/5733220/how-to-add-mingw-bin-directory-to-my-system-path
http://stackoverflow.com/questions/16716454/setting-up-mingw-and-codeblocks-in-windows-8-64-bit

# Enthought
https://www.enthought.com/
https://www.enthought.com/products/epd/free/


# Instructions for installing MiKTeX:
download installer from:
	http://miktex.org/download
Run installer

Now you need to configure MiKTeX
(follow isntructions from:
	http://wiki.lyx.org/FAQ/LaTeXErrors
)
Run the Miktex Package Manager as administrator:
	C:\Program Files\MiKTeX 2.9\miktex\bin\x64\mpm_mfc_admin.exe
Go to Repository -> 
	Change Package Repository -> 
	Packages should be installed from the internet -> 
	Connection Settings
Select an internet repository

Run the Miktex Settings Administrator:
	C:\Program Files\MiKTeX 2.9\miktex\bin\x64\mo_admin.exe
Refresh and Update formats

Run the command in cmd:
	kpsewhich article.cls
It should return the path to that file

Now when you preview in Lyx, 
Windows will spring up asking you to allow Miktex to make changes, 
and you should see packages being installed on the lyx status line at the bottom.



# Instructions for installing Lyx:
From the site:
	http://www.lyx.org/Download#toc3
download basic installer: 
	LyX-2.0.6-Installer-2.exe (~35 MB)
	not the Bundle installer
Run the installer "as administrator"
When prompted for latex.exe, point it to:
	C:\Program Files\MiKTeX 2.9\miktex\bin\x64\latex.exe


Now add PATH: in LYX go to:
Tools-> Preferences-> Paths-> PATH
and add the path to R, that can be found in R with the command: R.home('bin')
	C:\Program Files\R\R-3.0.1\bin\x64

In LYX, run Tools -> Reconfigure and restart LYX

(follow isntructions from:
	http://wiki.lyx.org/LyX/LyxWithRThroughSweave
)