As of May 31, 2023, we have updated our Code of Conduct.

# Tag Info

Accepted

### What activation function does the human brain use?

The thing you were reading about is known as the action potential. It is a mechanism that governs how information flows within a neuron. It works like this: Neurons have an electrical potential, ...
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### How to choose an activation function for the hidden layers?

It seems to me that you already understand the shortcomings of ReLUs and sigmoids (like dead neurons in the case of plain ReLU). You may want to look at ELU (exponential linear units) and SELU (self-...

### What are the advantages of ReLU vs Leaky ReLU and Parametric ReLU (if any)?

Combining ReLU, the hyper-parameterized1 leaky variant, and variant with dynamic parametrization during learning confuses two distinct things: The comparison between ReLU with the leaky variant is ...
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### Are softmax outputs of classifiers true probabilities?

The answer is both yes, and no. Or, to put it another way, the answer depends on what exactly you mean by "represent probabilities", and there is a valid sense in which the answer is yes, ...
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### What does it mean for a neuron in a neural network to be activated?

A neuron is said activated when its output is more than a threshold, generally 0. For examples : \begin{equation} y = Relu(a) > 0 \end{equation} when \begin{equation} a = w^Tx+b > 0 \end{...

### What is the purpose of an activation function in neural networks?

If you only had linear layers in a neural network, all the layers would essentially collapse to one linear layer, and, therefore, a "deep" neural network architecture effectively wouldn't be deep ...

### Are softmax outputs of classifiers true probabilities?

Excellent question. The simple answer is no. Softmax actually produces uncalibrated probabilities. That is, they do not really represent the probability of a prediction being correct. What usually ...

### Why is the derivative of the activation functions in neural networks important?

Consider a dataset $\mathcal{D}=\{x^{(i)},y^{(i)}:i=1,2,\ldots,N\}$ where $x^{(i)}\in\mathbb{R}^3$ and $y^{(i)}\in\mathbb{R}$ $\forall i$ The goal is to fit a function that best explains our dataset....
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### Do neurons of a neural network model a linear relationship?

In a neural network (NN), a neuron can act as a linear operator, but it usually acts as a non-linear one. The usual equation of a neuron $i$ in layer $l$ of an NN is o_i^l = \sigma(\mathbf{x}_i^l \...

### Why is no activation function used at the final layer of super-resolution models?

I am not into the field of super-resolution, but I think this question applies to general neural network construction. Usually, you try to solve a classification problem or a regression problem with ...

### Why is the derivative of the activation functions in neural networks important?

If what you are asking is what is the intuition for using the derivative in backpropagation learning, instead of an in-depth mathematical explanation: Recall that the derivative tells you a function'...
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### Why do ResNets avoid the vanishing gradient problem?

Before proceeding, it's important to note that ResNets, as pointed out here, were not introduced to specifically solve the VGP, but to improve learning in general. In fact, the authors of ResNet, in ...

### What activation function does the human brain use?

The brains of mammals do not use an activation function. Only machine learning designs based on the perceptron multiply the vector of outputs from a prior layer by a parameter matrix and pass the ...

### Why do activation functions need to be differentiable in the context of neural networks?

No, it is not necessary that an activation function is differentiable. In fact, one of the most popular activation functions, the rectifier, is non-differentiable at zero! This can create problems ...

### Can LSTM model use ReLU or LeakyReLU as the activation funtion?

Yes an LSTM can use any of these. There are no hard rules of which to use. That is why they all exist. Some rules of thumb are: Relu is the cheapest computationally. Almost always worth trying first. ...