Introduction to Neural Networks

A Mathematical PerspectiveAbstractIn this lecture, I aim to provide a first look at the mathematics that form the basis of neural networks’ operation, design, and implementation. My goal is to clarify some of the algorithms and computational methods that are used in these models, in the hope of providing you with a clearer understanding of the essential mathematical concepts involved. This lecture will in no way cover every aspect of neural networks, it could serve as a starting point for further learning.The lecture will cover the following:

• -  The use of summation notation

• -  A Basic Introduction to Linear Algebra

• -  An overview of the various components of a Neural Network

• -  The process involved in training the model

• -  Understanding the role of the activation functions

• -  A quick overview of the Gradient Descent Optimization Technique

Prerequisites:
You are expected to know about mathematical functions.