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Mark Stoddard's Technical Blog

Mark Stoddard Technical Blog

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Mark Stoddard's Technical Blog | markstoddard.com Reviews

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Mark Stoddard Technical Blog

INTERNAL PAGES

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1

Mark Stoddard's Technical Blog

http://www.markstoddard.com/article_9/A_Simple_Q_Learning_Example.html

A Simple Q Learning Example. Let us create a simple robot with six legs. We will use Q learning to teach this robot how to walk. A robot with 6 legs can walk without losing full support of three or more legs. A 4 legged robot would be a more complicated balancing act in a statically moving robot.

2

Mark Stoddard's Technical Blog

http://www.markstoddard.com/article_13/Testing_a_Movie_in_HTML5.html

Testing a Movie in HTML5. Testing a movie in HTML5 without any Flash or Javascript. Apparently, Firefox doesn't like mp4 delivered via HTML5. Couldn't get it to play in Chrome either. What is below is a webm movie of my cat taking care of my parents' cat. It is not difficult to convert from mp4 to webm using http:/ video.online-convert.com/convert-to-webm. Your browser does not support the video tag.

3

Mark Stoddard's Technical Blog

http://www.markstoddard.com/article_10/Building_a_Probabilistic_Dictionary_from_a_Bilingual_Text.html

Building a Probabilistic Dictionary from a Bilingual Text. DISCLAIMER* This is graduate level material involving heavy use of complex data structures and computer science theory. First of all, it is important that we discuss bit accuracy and the inability of a 64 bit computer to calculate probabilities of a 20,000 word text. For this, it will be necessary to compute our probabilities using log(probability) instead of using the actual probabilities. I have written a short tutorial. He was a Berserk. Note ...

4

Mark Stoddard's Technical Blog

http://www.markstoddard.com/article_11/Building_a_Simple_Naive_Bayes_Classifier.html

Building a Simple Naive Bayes Classifier. The information is a long list of 1s and 0s for whether or not the customer bought the various items and then a job classification. We will count up the number of times mechanics bought each item and the number of times non-mechanics (builders) bought each item. This will give us probabilities as such:. P(bought wrench mechanic) = count of mechanics who bought wrenches/ count of mechanics. The counting will also give us a prior:. P(features builder) = p(bought wr...

5

Mark Stoddard's Technical Blog

http://www.markstoddard.com/article_12/A_Simple_Curve_Fitting_Algorithm.html

A Simple Curve Fitting Algorithm. What I did will require a limited knowledge of statistics and a limited knowledge of calculus. The constraints were that all X values were limited by an upper and lower bound. Aside from that, the nature of the relation between X and Y were completely unknown. The technique I used will also be fairly noise tolerant given that there are enough data points. So, how do we take this technique and expand it to create a curve fit? This technique is good for curve fitting when ...

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Building a pneumatic power hammer. Testing a Movie in HTML5. A Simple Curve Fitting Algorithm. This curve fitting algorithm will match curves of unknown type and curves that have no knowable function. This will create probability curves where a probability is dependent upon a known variable. This algorithm is memory efficient. Building a Simple Naive Bayes Classifier. A Naive Bayes classifier is a great introduction to machine learning. This is a simple probabilistic supervised learning algorithm. Here w...

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