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Quantitative Finance · शब्दावली

Neural network, multilayer perceptron, activation function क्या है?

अन्य नाम: neural network · multilayer perceptron · activation function

Definition 7.1 Machine Learning for Markets · अध्याय 7 — Neural Networks for Noisy Tabular Data

A neural network is a function built by composing parametrised linear maps with fixed nonlinear functions. A multilayer perceptron (MLP) is the fully connected case: z0=xz_0 = x, zl=act(Alzl−1+cl)z_l = \mathrm{act}(A_lz_{l-1} + c_l) for l=1,…,Ll = 1,\dots,L, and fθ(x)=AL+1zL+cL+1f_\theta(x) = A_{L+1}z_L + c_{L+1}, with parameters θ=(Al,cl)l\theta = (A_l, c_l)_l. The activation function act\mathrm{act} is applied coordinate by coordinate; the usual one is relu(u)=max⁡(u,0)\mathrm{relu}(u) = \max(u, 0).

The chapter’s small network (drawn with fewer units than it has): 40 inputs, hidden layers of 32 and 16 units, one output, about 1 850 parameters. The larger network adds a first layer of 64 units.
Figure 7.1. The chapter’s small network (drawn with fewer units than it has): 40 inputs, hidden layers of 32 and 16 units, one output, about 1 850 parameters. The larger network adds a first layer of 64 units.
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