Table of Contents
7 relations: Artificial neuron, Backpropagation, Gradient, Loss function, Neural network (machine learning), Newton's method, Parabola.
Artificial neuron
An artificial neuron is a mathematical function conceived as a model of biological neurons in a neural network. Quickprop and artificial neuron are artificial neural networks.
See Quickprop and Artificial neuron
Backpropagation
In machine learning, backpropagation is a gradient estimation method used to train neural network models. Quickprop and backpropagation are artificial neural networks and machine learning algorithms.
See Quickprop and Backpropagation
Gradient
In vector calculus, the gradient of a scalar-valued differentiable function f of several variables is the vector field (or vector-valued function) \nabla f whose value at a point p gives the direction and the rate of fastest increase.
Loss function
In mathematical optimization and decision theory, a loss function or cost function (sometimes also called an error function) is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
See Quickprop and Loss function
Neural network (machine learning)
In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. Quickprop and neural network (machine learning) are artificial neural networks and Computational neuroscience.
See Quickprop and Neural network (machine learning)
Newton's method
In numerical analysis, Newton's method, also known as the Newton–Raphson method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.
See Quickprop and Newton's method
Parabola
In mathematics, a parabola is a plane curve which is mirror-symmetrical and is approximately U-shaped.