Layer: Difference between revisions

Layer (view source)

132 bytes removed , 28 February 2023

7,785

edits

@@ Line 27: / Line 27: @@
 ==How Layers Work in Neural Networks==
-Each layer in a neural network performs computations on data received from the previous layer, using weights and biases. These operations can be described mathematically as an activation function which maps inputs to outputs. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions which measure differences between predicted outputs and actual ones.
+Each layer in a neural network performs computations on data received from the previous layer. A layer's computation can be represented as the dot product of inputs and weights, followed by application of an activation function. The outputs from a layer are then fed back into the next one in the network, with this cycle repeated until an accurate final output is produced. These weights and biases are learned by the network through training, where its parameters are updated in order to minimize loss functions which measure differences between predicted outputs and actual ones.
-A layer's computation can be represented as the dot product of inputs and weights, followed by application of an activation function. The outputs from a layer are then fed back into the next one in the network, with this cycle repeated until an accurate final output is produced.
 ==What is a Layer?==