# Tag Info

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### 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-...
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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 ...
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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 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....

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

Training While "running" a neural network can be done with any activation functions, we usually want to train it - i.e., adjust its parameters so that the result would be closer to what we desire. ...

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

Consider a very simple neural network, with just 2 layers, where the first has 2 neurons and the last 1 neuron, and the input size is 2. The inputs are $x_1$ and $x_1$. The weights of the first layer ...

### What does it mean for a neuron in a neural network to be activated?

The term "activated" is mostly used when talking about activation functions which only outputs a value (except 0) when the input to the activation function is greater than a certain treshold. ...
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### Why can't the XOR linear inseparability problem be solved with one perceptron like this?

It can be done. The activation function of a neuron does not have to be monotonic. The activation that Rahul suggested can be implemented via a continuously differentiable function, for example \$ f(s)...
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### What is a "logit probability"?

Indeed I haven't seen the term "logit probability" used in many places other than that specific paper. So, I cannot really comment on why they're using that term / where it comes from / if anyone else ...

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

The basic (and usual) algorithm used to update the weights of the artificial neural network (ANN) is an iterative, numerical and optimization algorithm, called gradient descent, which is based on and ...

### Do neurons of a neural network model a linear relationship?

Almost never. The sum of linear functions is another linear function, so if neurons were only linear transformations there would be basically no point to having more than one neuron per layer. Instead,...