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programmingopiethehokie.com

programming opiethehokie

Results of my programming experiments, discussion of other computer science topics I am learning more about, and writing practice.

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programming opiethehokie | programmingopiethehokie.com Reviews
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Results of my programming experiments, discussion of other computer science topics I am learning more about, and writing practice.
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6 other work
7 extinction
8 devops handbook
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programming opiethehokie | programmingopiethehokie.com Reviews

https://programmingopiethehokie.com

Results of my programming experiments, discussion of other computer science topics I am learning more about, and writing practice.

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programmingopiethehokie.com programmingopiethehokie.com
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programming opiethehokie: November 2014

http://www.programmingopiethehokie.com/2014_11_01_archive.html

Friday, November 14, 2014. In my previous post on tf-idf. Or LSH. Today I'll summarize my LSH notes. In order to find these lexically similar documents, you need to represent each document as a set of sentences or phrases. One technique for this is called shingling. Shingle is any substring with length k. In the document. The value of k. Calculations. Consider a matrix where there is a row for each shingle and a column for every document sets. The matrix has a 1 for a particular row and column if...Then ...

2

programming opiethehokie: October 2014

http://www.programmingopiethehokie.com/2014_10_01_archive.html

Sunday, October 26, 2014. An example use case would be a web crawler that needs to determine whether is has already visited a URL. A small percentage of false positives will be acceptable because no significant portion of the web has only one link (read one incorrect bloom filter lookup) pointing to it. Is the number of bits in the array and k. Is the number of hash functions and n. Is the number of elements, then the probability of a false positive is:. Has to be bigger than n. Friday, October 10, 2014.

3

programming opiethehokie: April 2015

http://www.programmingopiethehokie.com/2015_04_01_archive.html

Sunday, April 26, 2015. Understanding Docker: Union File Systems. Earlier this week I took a Docker class at work. Having used Docker before I already knew some of the material, but that was a good thing because it gave me more of a chance to understand how Docker works. In this post I want to write about the idea of a union file system and how that relates to Docker images. To combine layers into an image Docker uses union file systems. Understanding Docker: Union File Systems. In my previous post Async...

4

Clustering | programming opiethehokie

http://www.programmingopiethehokie.com/2014/11/clustering.html

Thursday, November 13, 2014. Is an unsupervised machine learning technique for grouping similar items. Not only are the items grouped as the term implies, but the groups are actually discovered by the algorithm. Similarity of the items can be measured in many different ways depending on how the data is represented. Three common measures are Cosine similarity. Examples of how clustering can be applied:. Detecting groups of customers with similar buying patterns. Identifying the source of a cholera outbreak.

5

Eigenvectors: Dimensionality Reduction | programming opiethehokie

http://www.programmingopiethehokie.com/2014/12/eigenvectors-dimensionality-reduction.html

Monday, December 15, 2014. This is another Coursera Mining Massive Datasets. Recap focused on dimensionality reduction. I found it to be one of the most difficult topics to follow in both classes. Maybe it's all the linear algebra that I only partially remember. Here I hope to make it through enough of the math so that dimensionality reduction at least starts to make sense. Given a matrix M. Principal component analysis (PCA). Finds the eigenvectors for M. Is the eigenvector of M. Is the identity matrix.

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programming opiethehokie

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Information Security

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The Programming Pages

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