Terminology

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Inputs	An input vector is the data given as one input to the algorithm. Written as x, with elements x_i, where i runs form 1 to the number of input dimensions, m.
Weights (W_i,j)	W_i,j are the weighted connections between nodes i and j. For neural networks these weights are analogous to the synapses in the brain. They are arranged into a matrix W.
Outputs	The output vector is y, with elements y_j, where j runs from 1 the number of output dimensions, n. We can write y(x, W) to remind ourselves that the output depends on the inputs to the algorithm and the current set of weights of the network.
Targets	The target vector t, with elements t_j, where j runs from 1 to the number of output dimensions, n, are the extra data that we need for supervised learning, since they provide the "correct" answers that the algorith is learning about.
Activation Function	For neural networks, g(-) is a mathematical function that describes the firing of the neuron as a response to the weighted inputs.
Error E	A function that computes the inaccuracies of the network as a function of the outputs y and targets t.

Zusammenfassung der Ressource

	Erstellt von hvrd1 vor mehr als 9 Jahre