Input Layer

The input layer is the first layer of a neural network, responsible for receiving the input data.

Each neuron in this layer corresponds to a specific feature or part of the input data, and this information is passed on to the next layer of the network (the hidden layer).

For instance, when processing a 5×5 grayscale image, the brightness value of each pixel is received by the input layer and sent to individual neurons.

The example below demonstrates how a 5×5 image is delivered to the neurons in the input layer.

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[
  [0, 0, 255, 0, 0],
  [0, 255, 0, 255, 0],
  [255, 0, 0, 0, 255],
  [0, 255, 0, 255, 0],
  [0, 0, 255, 0, 0]
]

Here, 0 represents black, and 255 represents white.

In neural networks, it is common to normalize the data between the values of 0 and 1 to facilitate easier learning.

For example, converting 255 to 1.0 and 0 to 0.0 is a commonly used approach.