Last Updated on August 28, 2021 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 me... Read more
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... Read more
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 l... Read more
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 com... Read more
Whether you implement a neural network yourself or you use a built in library for neural network learning, it is of paramount importance to understand the significance of a sigmoid function.... Read more
Last Updated on August 10, 2021 Whether it is a supervised learning problem or an unsupervised problem, there will be some optimization algorithm working in the background. Almost any classi... Read more
The method of Lagrange multipliers is a simple and elegant method of finding the local minima or local maxima of a function subject to equality or inequality constraints. Lagrange multiplier... Read more
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 applyin... Read more
Last Updated on August 18, 2021 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 backpropa... Read more
Last Updated on August 5, 2021 Hessian matrices belong to a class of mathematical structures that involve second order derivatives. They are often used in machine learning and data science a... Read more
