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In the course of passing through the intermediate layers to the exit, they are subject to adjustment until the same results are achieved in each successive experiment. Link weights are needed to determine the importance of a particular variable in the input layer. Passing through intermediate layers multiplies the value of the original data by the weight of the connection, then the results are summed up. If the resulting total exceeds the specified threshold, the activated neuron passes the data to the next level.
For clarity we will analyze the principle of operation of a neural Buy Cell Phone Number List
network using a specific example. Suppose we want to get an answer to the question of whether it is worth going for mushrooms on the weekend. There can be two answers: “yes” - 1 and “no” - 0. Decision making (y-hat) depends on three factors, which we will present in the form of questions: has the mushroom season started? ("yes" - 1 and "no" - 0); is the place popular? ("yes" - 1 and "no" - 0); Is heavy rain forecast for the weekend? ("yes" - 0 and "no" - 1). Let's say we have the following input data: X1 = 1 because the season is in full swing; X2 = 0, since few people know about this forest; X3 = 1 because weather forecasters are predicting dry weather.
Next we assign weights to these values to establish their significance. Now we need to give these values some "weights" to determine their importance. Let's prioritize as follows: W1 = 5, since the season does not last long; W2 = 2, since other mushroom pickers are not a hindrance; W3 = 4, since picking mushrooms in a downpour is unpleasant. We will denote the threshold value as 3, therefore, the offset value will be - 3. We substitute the values into the formula to obtain the desired result: Y-hat = (1*5) + (0*2) + (1*4) - 3 = 6. We got a total that exceeds the threshold value.
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发表于 2023-7-24 17:26:53