The following are few quote written by Albert Einstein:
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Out of clutter, find simplicity. From discord, find harmony. In the middle of difficulty lies opportunity.
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Few are those who see with their own eyes and feel with their own hearts.
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The world is a dangerous place, not because of those who do evil, but because of those who look on and do nothing.
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I am enough of an artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.
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Perfections of means and confusion of goals seem-in my opinion-to characterize our age.
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It's not that I'm so smart, it's just that I stay with problems longer.
Download Library: Code
The use of neural network is increasing, since scientists and engineers are able to achieve effective results that enable neural networks to be used in the industry.
You will find dozens of Neural Network libraries on the net, this library is written in C#, I wrote it when I was a student in university in 2003, the importance of this library is that it is simple to extend and maintain, and the code naming refers to neural network terminology, this makes easier for students and developers that have experience in the field to be able to work their way easily in the library.
The most important thing to keep in mind when specifying a neural network is the following:
1- Study the data; try to find patterns in the data that enables the classification of this data.
2- Massage noisy data, data massaging for noisy data is a must, since real world problems, are subject to lots of noisy data.
3- Specify a structure for the neural network, relative to data.
4- Choose the correct sample of data for training the network, the data should not all be similar. Best training procedure is to compile a wide range of examples that exhibit all the different characteristics we are interested in. It is important to select examples which do not have major dominant features which are of no interest to us, but are common to our input data.
5- The neural network should be trained to a specific curve, too much learning, will cause the neural network to memorize specific patterns, and fail in classifying similar patterns, for example a simple case is the OCR optical character recognition, and the hand written problem.
These are the basic general points that should be studied when trying to create an effective solution using neural networks.
"An artificial neural network (ANN), usually called "neural network" (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase."(Wikipedia)
The following is Implementation of the multi-layered artificial neural network, and SOM Self Organizing Map Kohonen.
Multi-layered artificial neural network ANN is the base network for SOM and other networks.
Neural network activation functions, a base class called AActivationFunction, is for activation function classes.
Using the this neural network library is very easy in few lines of code, one will be able to setup a network and configure it for training and testing.
Training and testing network is very easy, and network weights can be saved and loaded easily.
Network.SetupData(trainingSample1);
Network.TrainNetwork();
Network.SetupData(testSample2);
Network.TestNetwork();
Download the library and read the documentation for further details.
This library is free for everyone to extend and use.
Regards
Rabeeh Abla