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Tuesday, 17 February 2015. The Genetic Algorithm - Explained. Genetic Algorithms are used to generate optimal solution by method of evolution-inspired search and optimization; Generally used in problems where linear/brute-force searches are not viable in terms of time, such as – Travelling Salesman Problem, Timetable Scheduling, Finding Neural Network Weights, Sudoku, Trees(data-structure) etc.  The first requirement is a DNA encoding scheme suitable for representing individuals, second re...

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Tuesday, 17 February 2015. The Genetic Algorithm - Explained. Genetic Algorithms are used to generate optimal solution by method of evolution-inspired search and optimization; Generally used in problems where linear/brute-force searches are not viable in terms of time, such as – Travelling Salesman Problem, Timetable Scheduling, Finding Neural Network Weights, Sudoku, Trees(data-structure) etc.  The first requirement is a DNA encoding scheme suitable for representing individuals, second re...
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Tech-Effigy | techeffigytutorials.blogspot.com Reviews

https://techeffigytutorials.blogspot.com

Tuesday, 17 February 2015. The Genetic Algorithm - Explained. Genetic Algorithms are used to generate optimal solution by method of evolution-inspired search and optimization; Generally used in problems where linear/brute-force searches are not viable in terms of time, such as – Travelling Salesman Problem, Timetable Scheduling, Finding Neural Network Weights, Sudoku, Trees(data-structure) etc.  The first requirement is a DNA encoding scheme suitable for representing individuals, second re...

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Bloom Filters - Explained ~ Tech-Effigy

http://www.techeffigytutorials.blogspot.com/2015/01/bloom-filters-explained.html

Sub Child Category 1. Sub Child Category 2. Sub Child Category 3. Thursday, 8 January 2015. Bloom Filters - Explained. M - number of elements in bit array. K - number of hash functions. N - number of items in collection. P - desired false positive probability / 0.0 - 1.0. M = -( n*ln(p) /(ln(2) 2) ;. K = (m/n) * ln(2);. N = (m * ln(2) /k;. P = e (-(m/n) * (ln(2) 2) ; / estimated probability of false positives. Lets do a test, i know n = 1000,. And i desire a p = 0.01 :. M = 9585.058 = 9586. K = 6644 = 7.

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Markov Chains - Explained ~ Tech-Effigy

http://www.techeffigytutorials.blogspot.com/2015/01/markov-chains-explained.html

Sub Child Category 1. Sub Child Category 2. Sub Child Category 3. Thursday, 8 January 2015. Markov Chains - Explained. To generate a simple prediction of events, we can say today is Cloudy, and from the table above we can determine that the next state will most likely be Rain with 50%, then we go to the Rain state, and the following day it will probably still be raining with 60%. Markov Chains can be represented as a state diagram, or a matrix called a Transition Matrix:. Here`s a quote from my 3rd Order...

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The Genetic Algorithm - Explained ~ Tech-Effigy

http://www.techeffigytutorials.blogspot.com/2015/02/the-genetic-algorithm-explained.html

Sub Child Category 1. Sub Child Category 2. Sub Child Category 3. Tuesday, 17 February 2015. The Genetic Algorithm - Explained. Genetic Algorithms are used to find optimal solution by method of evolution-inspired search and optimization; Generally used in problems where linear/brute-force searches are not viable in terms of time, such as – Travelling Salesman Problem, Timetable Scheduling, Finding Neural Network Weights, Sudoku, Trees(data-structure) etc. The first requirement is a encoding sch...Step 3:...

4

Neural Network Illustrated – Step by Step ~ Tech-Effigy

http://www.techeffigytutorials.blogspot.com/2015/01/neural-network-illustrated-step-by-step.html

Sub Child Category 1. Sub Child Category 2. Sub Child Category 3. Thursday, 8 January 2015. Neural Network Illustrated – Step by Step. Are the inputs scaled to [-1,1] or [0, 1], depending on the activation function used. Activation Function=Tanh(), Sigmoid() or any differential-able function. Current neurons input weights, initialized randomly between [-1, 1]. Bias Weight, connected to nothing, used as a threshold, initialized same as W. The output of the current neuron. Output Neurons Previous Output.

5

January 2015 ~ Tech-Effigy

http://www.techeffigytutorials.blogspot.com/2015_01_01_archive.html

Sub Child Category 1. Sub Child Category 2. Sub Child Category 3. Thursday, 8 January 2015. Neural Network Illustrated – Step by Step. Are the inputs scaled to [-1,1] or [0, 1], depending on the activation function used. Activation Function=Tanh(), Sigmoid() or any differential-able function. Current neurons input weights, initialized randomly between [-1, 1]. Bias Weight, connected to nothing, used as a threshold, initialized same as W. The output of the current neuron. Markov Chains - Explained.

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Tuesday, 17 February 2015. The Genetic Algorithm - Explained. Genetic Algorithms are used to generate optimal solution by method of evolution-inspired search and optimization; Generally used in problems where linear/brute-force searches are not viable in terms of time, such as – Travelling Salesman Problem, Timetable Scheduling, Finding Neural Network Weights, Sudoku, Trees(data-structure) etc.  The first requirement is a DNA encoding scheme suitable for representing individuals, second re...

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