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The derivative of with respect to yields the state equation as shown before, and the state variable is =. The derivative of L {\displaystyle {\mathcal {L}}} with respect to u {\displaystyle u} is equivalent to the adjoint equation, which is, for every δ u ∈ R m {\displaystyle \delta _{u}\in \mathbb {R} ^{m}} ,
To find the right derivative, we again apply the chain rule, this time differentiating with respect to the total input to , : = () Note that the output of the j {\displaystyle j} th neuron, y j {\displaystyle y_{j}} , is just the neuron's activation function g {\displaystyle g} applied to the neuron's input h j {\displaystyle h_{j}} .
For backpropagation, the activation as well as the derivatives () ′ (evaluated at ) must be cached for use during the backwards pass. The derivative of the loss in terms of the inputs is given by the chain rule; note that each term is a total derivative , evaluated at the value of the network (at each node) on the input x {\displaystyle x} :
Rprop, short for resilient backpropagation, is a learning heuristic for supervised learning in feedforward artificial neural networks. This is a first-order optimization algorithm. This algorithm was created by Martin Riedmiller and Heinrich Braun in 1992. [1]
In 1986, David E. Rumelhart et al. popularised backpropagation but did not cite the original work. [29] [8] In 2003, interest in backpropagation networks returned due to the successes of deep learning being applied to language modelling by Yoshua Bengio with co-authors. [30]
The function was tabulated by Vera Faddeeva and N. N. Terentyev in 1954. [8] It appears as nameless function w(z) in Abramowitz and Stegun (1964), formula 7.1.3. The name Faddeeva function was apparently introduced by G. P. M. Poppe and C. M. J. Wijers in 1990; [9] [better source needed] previously, it was known as Kramp's function (probably after Christian Kramp).
The source code for a function is replaced by an automatically generated source code that includes statements for calculating the derivatives interleaved with the original instructions. Source code transformation can be implemented for all programming languages, and it is also easier for the compiler to do compile time optimizations.
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial derivative of the loss function. [1]