Understanding the underlying workings and components of AI can prepare you and allow you to build better digital tools, products and security as they can help you gain most if not all the knowledge of what is in your datasets, how to setup optimal data structures for training and inference and provide robust automated systems for your customers.

Neural networks is the latest technology when it comes to machine learning. In more classical machine learning systems you would set up a large data structure as in like an array/matrix or a network graph for example, once your dataset was structured you would apply a function just like you did on your early days of math and then present a scatter plot on a graph with the line of best fit, in those days of machine learning those points closer to the line of best fit were and still in some systems today are how the best predictions are presented to you. Neural networks have taken it a slightly bit further and while the virtual concept aims to model the human brain (in classical machine learning the model was the equation) it is important to keep in mind that even though powerful, it is a virtual concept as we have had for decades with digital devices and electronics hence why having a true insight of the technology creates better automation, applications and even robotics. Modeling in neural networks is multiplying your numerical inputs also by another structured matrix (one example would be an identity matrix which is a special type of diagonal matrix) and then adding or subtracting numerical values that may give your inputs certain properties to then iterate over them as layers of these computations either a few, several or many times, lastly applying the calculus chain rule backwards through the iterations of layers to get the rates of change and see how the network influences the end output.