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Gradient descent
Mini batch gradient descent
For t=1, …, number_of_batches:
Vectorized Forward prop on $X^{t}$ $Z^{[1]} = W^{[1]} X^{t} + b^{[1]}$ $A^{[1]} = g^{[1]}(Z^{[1]})$ ... $A^{[L]} = g^{[L]}(Z^{[L]})$ Compute cost $J^{[t]}$ = 1/1000 * ... Backprop to compute gradients for $J^{[t]}$ Update weights $W^{[l]} = W^{[l]} - \alpha d W^{[l]}; b^{[l]} = b^{[l]} - \alpha d b^{[l]}$
Exponentially weighted averages
$V_t = \beta V_{t-1} + (1-\beta) \Theta_t$
$\beta = 0.98$ is smoother
Gradient Descent with Momentum
RMSprop
Root mean squared