The sigmoid function, \(\sigma(x)\), is also called the logistic function, or expit [1]. It is the inverse of the logit function. Its function definition is: \[ \sigma(x) = \frac{1}{1+e^{-x}} \tag{1}\] Let’s get familiar by plotting this function first: And just to be complete, here is the python code that produced the above plot: We see
The term logit has different meanings in math and in the TensorFlow library. In TensorFlow it means “Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities.” What this means is: the logits are the vector you get as output of the last layer of your neural network. It is
You can use software, like scipy.stats.beta if you want to sample from a Beta distribution. But you can also create a Beta distribution yourself — from scratch. The only thing you need is access to a Uniform random variable. This post shows an implementation of example 12.1.4 on p. 539 of the book “Introduction to
A detailed solution for a problem about total probability
This article aims to distinguish between Variance, Covariance, Correlation, Autocovariance, and Autocorrelation. Furthermore there will be a numerical discrete example for computing the Autocorrelation in python. This article will not derive the formulae mathematically, only state them. Variance measures the spread of a r.v. (random variable). \[\begin{equation} Var(X) = E[( X-\bar{x} )^2] = \sum_{i=1}^{n} {
