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