17 lines
519 B
Plaintext
17 lines
519 B
Plaintext
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The idea is always the same:
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<l2st>
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Define complicated model to learn (often millions of parameters)
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Define loss function that this model should minimize (example: $\sum_i (y_i-f(x_i))^2$)
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Find parameters that minimize the loss (->Backpropagation)
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</l2st>
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Usually Neural Networks:
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<l2st>
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$f(x)=f_n(x)=activation(A_n\cdot f_{n-1}(x)+b_n)$
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$f_0(x)=x$
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</l2st>
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Powerful, as you can show that when there are 3 Layers+ (and infinitely sized matrices), you can approximate any function
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->So a model becomes a loss function
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