The chain rule allows us to find the derivative of composite functions. It is computed extensively by the backpropagation algorithm, in order to train feedforward neural networks. By applying the chain rule in an efficient manner while following a specific order of operations, the backpropagation algorithm calculates the error gradient of the loss function with respect to each weight of the network.  In this tutorial, you will discover the chain rule of calculus for univariate and multivariate functions. After completing […]

The chain rule is an important derivative rule that allows us to work with composite functions. It is essential in understanding the workings of the backpropagation algorithm, which applies the chain rule extensively in order to calculate the error gradient of the loss function with respect to each weight of a neural network. We will be building on our earlier introduction to the chain rule, by tackling more challenging functions.  In this tutorial, you will discover how to apply the […]

When it comes to machine learning tasks such as classification or regression, approximation techniques play a key role in learning from the data. Many machine learning methods approximate a function or a mapping between the inputs and outputs via a learning algorithm. In this tutorial, you will discover what is approximation and its importance in machine learning and pattern recognition. After completing this tutorial, you will know:  What is approximation Importance of approximation in machine learning Let’s get started.

A Gentle Introduction to Taylor Series Taylor series expansion is an awesome concept, not only the world of mathematics, but also in optimization theory, function approximation and machine learning. It is widely applied in numerical computations when estimates of a function’s values at different points are required. In this tutorial, you will discover Taylor series and how to approximate the values of a function around different points using its Taylor series expansion. After completing this tutorial, you will know: Taylor […]

An artificial neural network is a computational model that approximates a mapping between inputs and outputs.  It is inspired by the structure of the human brain, in that it is similarly composed of a network of interconnected neurons that propagate information upon receiving sets of stimuli from neighbouring neurons. Training a neural network involves a process that employs the backpropagation and gradient descent algorithms in tandem. As we will be seeing, both of these algorithms make extensive use of calculus. […]

In a previous post, we introduced the method of Lagrange multipliers to find local minima or local maxima of a function with equality constraints. The same method can be applied to those with inequality constraints as well. In this tutorial, you will discover the method of Lagrange multipliers applied to find the local minimum or maximum of a function when inequality constraints are present, optionally together with equality constraints. After completing this tutorial, you will know How to find points […]