The Swish activation function is a non-linear function that can be used to improve the performance of neural networks. It is defined as follows: where sigmoid(x) is the sigmoid function: The Swish activation function was first proposed by Google Brain in 2017. It has since been used to achieve state-of-the-art results on a variety of …
Activation functions play a crucial role in artificial neural networks (ANNs) by introducing nonlinearity to the model, enabling it to learn complex patterns and solve a wide range of problems. The mathematics behind activation functions involves various functions that transform the input signal (weighted sum of inputs) into an output signal. Here, we will discuss …
Maximum Likelihood Estimation (MLE) is a statistical method used to find the parameter values that maximize the likelihood of obtaining observed data. The likelihood is a measure of how probable the observed data is, given a specific set of parameter values for a statistical model. In other words, MLE aims to find the parameters that …
Maximum Likelihood, log likelihood and reconstruction error Read More »
