Activation Functions (C1W3L06)

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I use always Leaky Relu, it gives good results while doing the model building.

thepresistence
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At 4:12 downsides of sigmoid and tanh:
how does gradient descent slow down when slope of sigmoid or tan-h becomes very small? I understand gradient descent takes smaller steps on graph of J(w) v/s w, as it approaches the minimum, but what does the slope of gradient descent have to do with slope of these functions?

SiddharthDesai
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The tanh definition is not correct at 9:01. The denominator should be exp(z) + exp(-z). So the whole function should be tanh = (exp(z) - exp(-z)) / (exp(z) + exp(-z))

abekang
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At 9:01 coordinates of tanh graph should be Z not X ????

saanvisharma
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But ReLU or even Leaky ReLU still are linear functions(but more complex ofc) which means they cant fit some complex classification problems, isn't it?

rpimxoimxo
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Clarification: Activation Function

From 7:58, this visual comparison of 4 activation functions. All 4 should have "z" as the horizontal axis. The top right chart in the slide shows "x", which should be "z".

manuel
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So I finally understand why certain activation functions are chosen, it's because we usually use them..

prismaticspace
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Can't we reassign the 0 labels to -1, and then use tanh() for the output layer?

pivasmilos
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Can we make the parameter we apply at Relu function a part of hidden learning parameters like the weights and bias?

Jaspinik
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Do we need an activation function when in case of regression problems where output is continuous values????

sreemantokesh
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A sigmoid function is one that have a "S" curve, is not a synonym of logístic regression, in fact tanh func is a type of a a sigmoid function, so min 2:58 is kind of incorrect the explaining. Apart of that nice vid.

hectorgarces
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Why we need an activation function in hidden layers?

সমুদ্রসন্তান
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what about Softmax ? is it better than RELU?

xerocool
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I am beginning to see math notation is simply just really poorly named variables in programming. Its too bad that the "one letter" variable naming in math notation has such deep roots. In programming we would call it "sigmoid()" but in math even when you run out of letters of the alphabet, we go to different alphabets...

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