Translations:Neural Networks/24/en: Difference between revisions

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    # '''Defining a loss function''' — a measure of how far the network's predictions are from the true targets (see [[Loss Functions]]).
    # '''Defining a {{Term|loss function}}''' — a measure of how far the network's predictions are from the true targets (see [[Loss Functions]]).
    # '''Forward pass''' — computing the output of the network for a given input by propagating values layer by layer.
    # '''Forward pass''' — computing the output of the network for a given input by propagating values layer by layer.
    # '''Backward pass (backpropagation)''' — computing the gradient of the loss with respect to every weight by applying the chain rule in reverse through the network (see [[Backpropagation]]).
    # '''Backward pass ({{Term|backpropagation}})''' — computing the gradient of the loss with respect to every weight by applying the chain rule in reverse through the network (see [[Backpropagation]]).
    # '''Parameter update''' — adjusting the weights using an optimisation algorithm such as [[Gradient Descent]] or one of its variants.
    # '''Parameter update''' — adjusting the weights using an optimisation algorithm such as [[Gradient Descent]] or one of its variants.
    # '''Iteration''' — repeating steps 2–4 over many passes (epochs) through the training data.
    # '''Iteration''' — repeating steps 2–4 over many passes ({{Term|epoch|epochs}}) through the training data.

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    Message definition (Neural Networks)
    # '''Defining a {{Term|loss function}}''' — a measure of how far the network's predictions are from the true targets (see [[Loss Functions]]).
    # '''Forward pass''' — computing the output of the network for a given input by propagating values layer by layer.
    # '''Backward pass ({{Term|backpropagation}})''' — computing the gradient of the loss with respect to every weight by applying the chain rule in reverse through the network (see [[Backpropagation]]).
    # '''Parameter update''' — adjusting the weights using an optimisation algorithm such as [[Gradient Descent]] or one of its variants.
    # '''Iteration''' — repeating steps 2–4 over many passes ({{Term|epoch|epochs}}) through the training data.
    1. Defining a loss function — a measure of how far the network's predictions are from the true targets (see Loss Functions).
    2. Forward pass — computing the output of the network for a given input by propagating values layer by layer.
    3. Backward pass (backpropagation) — computing the gradient of the loss with respect to every weight by applying the chain rule in reverse through the network (see Backpropagation).
    4. Parameter update — adjusting the weights using an optimisation algorithm such as Gradient Descent or one of its variants.
    5. Iteration — repeating steps 2–4 over many passes (epochs) through the training data.
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