Backpropagation Program Telewizyjny
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Back-propagation is the most common algorithm used to train neural networks. There are many ways that back-propagation can be implemented. This article presents a code implementation, using C#, which closely mirrors the terminology and explanation of back-propagation given in the on the topic.You can think of a neural network as a complex mathematical function that accepts numeric inputs and generates numeric outputs. The values of the outputs are determined by the input values, the number of so-called hidden processing nodes, the hidden and output layer activation functions, and a set of weights and bias values.A fully connected neural network with m inputs, h hidden nodes, and n outputs has (m. h) + h + (h.
n) + n weights and biases. For example, a neural network with 4 inputs, 5 hidden nodes, and 3 outputs has (4. 5) + 5 + (5. 3) + 3 = 43 weights and biases.
Training a neural network is the process of finding values for the weights and biases so that, for a set of training data with known input and output values, the computed outputs of the network closely match the known outputs.The best way to see where this article is headed is to examine the demo program in Figure 1. The demo program begins by generating 1,000 synthetic data items. Each data item has four input values and three output values. For example, one of the synthetic data items is:Click on image for larger view. Figure 1. Back-Propagation Training in Action-1.09 -9.10 0.85 5.52 0 0 1The four input values are all between -10.0 and +10.0 and correspond to predictor values that have been normalized so that values below zero are smaller than average, and values above zero are greater than average. The three output values correspond to a variable to predict that can take on one of three categorical values.
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For example, you might want to predict the political leaning of a person: conservative, moderate or liberal. Using 1-of-N encoding, conservative is (1, 0, 0), moderate is (0, 1, 0), and liberal is (0, 0, 1). So, for the example data item, if the predictor variables are age, income, education, and debt, the data item represents a person who is younger than average, has much lower income than average, is somewhat more educated than average, and has higher debt than average.
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Program Telewizyjny Polsat
The person has a liberal political view.