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Why don’t they use all kinds of non-linear functions in Neural Network Activation Functions? [duplicate]

Pardon my ignorance, but after just learning about Sigmoid and Tanh activation functions (and a few others), I am wondering why they choose functions that always go up and to the right? Why not use all kinds of crazy input functions, those that fluctuate up and down, ones that are directed down instead of up, etc.? What if used functions like those in your neurons, what is the problem, why isn’t it done? Why do they stick to very primitive very simple functions?

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Why isn’t there just one “keystone” activation function in Neural Networks?

This article says the following:

Deciding between the sigmoid or tanh will depend on your requirement of gradient strength.

I have seen (so far in my learning) 7 activation functions/curves. Each one seems to be building on the last. But then like the quote above, I have read in many places essentially that "based on your requirements, select your activation function and tune it to your specific use case".

This doesn’t seem scalable. From an engineering perspective, a human has to come in and tinker around with each neural network to find the right or optimal activation function, which seems like it would take a lot of time and effort. I’ve seen papers which seem to describe people working on automatically finding the "best" activation function for a particular data set too. From an abstraction standpoint, it’s like writing code to handle each user individually on a website, independently of the others, rather than just writing one user authentication system that works for everyone (as an analogy).

What all these are papers/articles are missing is an explanation of why. Why can’t you just have one activation function that works in all cases optimally? This would make it so engineers don’t have to tinker with each new dataset and neural network, they just create one generalized neural network and it works well for all the common tasks today’s and tomorrow’s neural networks are applied to. If someone finds a more optimal one, then that would be beneficial, but until the next optimal one is found, why can’t you just use one neural network activation function for all situations? I am missing this key piece of information from my current readings.

What are some examples of why it’s not possible to have a keystone activation function?

Why do we need to take the derivative of the activation function in backwards propagation?

I was reading this article here:

When he gets to the part where he calculates the loss at every node, he says to use the following formula:

delta_0 = w . delta_1 . f'(z) where values delta_0, w and f’(z) are those of the same unit’s, while delta_1 is the loss of the unit on the other side of the weighted link.”

And $ f$ is the activation function.

He then says:

“You can think of it this way, in order to get the loss of a node (e.g. Z0), we multiply the value of its corresponding f’(z) by the loss of the node it is connected to in the next layer (delta_1), by the weight of the link connecting both nodes.”

However, he doesn’t actually explain why we need the derivative term. Where does that term come from and why do we need it?

My idea so far is this:

The fact that the identity activation function causes the term to disappear is a hint. The node doesn’t feed into the next exactly as is, it depends on the activation function. When the activation function is the identity, the loss at that node just passes to the next one based on the weight. Basically, you just need to factor in the activation function somehow, specifically in a way that doesn’t matter when it’s the identity, and of course the derivative is a way to do this.

The issue is that this isn’t very rigorous, so I’m looking for a slightly more detailed explanation.

Why is my plastic credit card and activation code sent separately?

Capital one recently sent my plastic credit card by paper mail and it’s activation code by a separate paper mail. What security problem does this mitigate? If a rogue element has access to my mail box or home, they will have both the plastic card as well as the activation code. The only thing I can think of is that they are preventing rogue elements on their side from having access to the two information at the same time? Or is it something else?

can’t launch matlab after activation ubuntu 18.04 Licensing error: -8,523

wided@wided:/usr/local/MATLAB/MATLAB_Production_Server/R2015a/bin$ sudo ./matlab License checkout failed. License Manager Error -8 Make sure the HostID of the license file matches this machine, and that the HostID on the SERVER line matches the HostID of the license file.

Troubleshoot this issue by visiting:

Diagnostic Information: Feature: MATLAB License path: /home/wided/.matlab/R2015a_licenses:/usr/local/MATLAB/MATLAB_Production_Server/R2015a/licenses/licen se.dat:/usr/local/MATLAB/MATLAB_Production_Server/R2015a/licenses/license_wided_161052_R2015a.lic Licensing error: -8,523. wided@wided:/usr/local/MATLAB/MATLAB_Production_Server/R2015a/bin$