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Input layer: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ==Introduction== The input layer is an essential element in any machine learning model. It takes input data and passes it on to the next layer in the model, where it is processed and transformed into meaningful information. In this article, we'll examine in depth the characteristics of an input layer as well as its role in overall machine learning model operation. ==Structure of the Input Layer== The input layer is typically the initi...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[Input layer]] is the first [[layer]] of a [[neural network]]. It contains the [[feature vector]] of the [[input data]] ([[example]]). | |||
The input layer is typically the initial stage in a neural network and consists of [[neuron]]s that process input data. Here, the number of neurons is equal to the number of [[features]] present in the input data - for instance, if an image has 28 height of pixels and 28 width of pixels, respectively, with three color channels (red, green and blue), then we would expect there to be 28 * 28 * 3 = 2352 neurons in this layer. | |||
The input layer is typically the initial stage in a neural network and consists of | |||
Each neuron in the input layer is connected to all neurons in the next, allowing it to pass on input data for processing. These connections between neurons are known as weights and represent | Each neuron in the input layer is connected to all neurons in the next [[layer]], allowing it to pass on input data for processing. These connections between neurons are known as [[weights]] and represent the strength of the connection. Weights are updated during [[training]] in order to minimize errors between actual [[output]] and desired output ([[label]]). | ||
==Activation Function in the Input Layer== | ==Activation Function in the Input Layer== | ||