Deep Learning

An artificial neural network (ANN) is a component of a computing system that mimics how the human brain analyzes and processes data. Artificial intelligence (AI) is built on this basis, and it solves issues that would be impossible or difficult to solve by human or statistical criteria. " " "

Genetic Algorithms are a type of optimization algorithms that falls under the category of evolutionary computations. In evolutionary computation, the algorithms are inspired by the biological evolution processes like reproduction, crossovers, selection, and mutation." "

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function (commonly called loss/cost functions in machine learning and deep learning). To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. If, instead, one takes steps proportional to the positive of the gradient, one approaches a local maximum of that function; the procedure is then known as gradient ascent. Gradient descent is also known as the steepest descent." "

Activation functions are mathematical equations that determine the output of a neural network model. They help the network to use the important information and suppress the noise." "

The softmax function is the generalized form of the sigmoid function. Softmax Activation Function is the mathematical function that converts the vector of numbers into the vector of the probabilities. Softmax Activation Function is commonly used as an activation function in the case of multi-class classification problems in machine learning. This article contains the softmax activation function for example."