What is ANIMA ( Adaptive Network Intelligence Management ):?

Adaptive Network Intelligence Management.

Acurate representational learning of both the explicit and implicit relationships within data is critical to the ability of machines to perform more complex and abstract reasoning tasks. 

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Where do Bitcoin come from?

New Bitcoins are created in a process called "mining," which involves Bitcoin users attempting to figure out a complex mathematical solution related to the cuttent number of Bitcoins. Like finding a missing piece of a puzzle. Whomever finds the puzzle piece wins a certain number of Bitcoins, and the process starts all over again.
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How To Implement Naive Bayes From Scratch in Python ?

#How To Implement Naive Bayes From Scratch in Python #http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/ #Dataset #https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data import csv import math import random #Handle data def loadCsv(filename): lines = csv.reader(open(filename, "r")) dataset = list(lines) for i in range(len(dataset)): dataset[i] = [float(x) for x in dataset[i]] return dataset #Test handling data """  filename = 'pima-indians-diabetes.data.csv' SomeDataset = loadCsv(filename) print("Loaded data file {0:s} with {1:5d} rows".format(filename,len(SomeDataset))) """ #Split dataset with ratio def splitDataset(dataset, splitRatio): trainSize = int(len(dataset) * splitRatio) trainSet = [] copy = list(dataset) while len(trainSet) < trainSize: index = random.randrange(len(cop...
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How To Create A Simple Hough Transform Matrix

A = [0 0 0 0 15; 0 0 0 15 0; 0 0 15 0 0; 0 15 0 0 0; 15 0 0 0 0]; [m,n] = size(A); rhoValues = zeros(m,n); thetaValues = zeros(m,n); diagonalDist = floor(sqrt(m^2 + n^2)); thetaMax = 90; rhoRange = -diagonalDist:diagonalDist; thetaRange = -thetaMax:thetaMax; %Matrix Hough houghMatrix = zeros(length(rhoRange), length(thetaRange)); for i = 1:m     for j = 1:n         if (A(i,j) ~= 0)             x = m - 1;             y = n - 1;             for theta = thetaRange                 rhoValues = round((x*cosd(theta)) + (y*sind(theta)));                 rhoIndex = rhoValues + diagonalDist + 1;                 thetaIndex = theta + thetaMax + 1;                 houghMatrix(rh...
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