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Gradient descent is a method for unconstrained mathematical optimization. ... "Gradient Descent, How Neural Networks Learn". 3Blue1Brown. October 16, 2017 ...
Backpropagation was first described in 1986, with stochastic gradient descent being used to efficiently optimize parameters across neural networks with multiple hidden layers. Soon after, another improvement was developed: mini-batch gradient descent, where small batches of data are substituted for single samples.
In machine learning, backpropagation [1] is a gradient estimation method commonly used for training a neural network to compute its parameter updates.. It is an efficient application of the chain rule to neural networks.
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While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that direction. A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.
steepest descent (with variable learning rate and momentum, resilient backpropagation); quasi-Newton (Broyden–Fletcher–Goldfarb–Shanno, one step secant); Levenberg–Marquardt and conjugate gradient (Fletcher–Reeves update, Polak–Ribiére update, Powell–Beale restart, scaled conjugate gradient). [4]
Recurrent neural networks ... Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. In neural networks, ...
Gradient descent methods are first-order, iterative, optimization methods. Each iteration updates an approximate solution to the optimization problem by taking a step in the direction of the negative of the gradient of the objective function.