A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.What you will gain from this book:* A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python.Who this book is for:* Beginners who want to fully understand how networks work, and learn to build two step-by-step examples in Python. * Programmers who need an easy to read, but solid refresher, on the math of neural networks.What’s Inside - ‘Make Your Own Neural Network: An Indepth Visual Introduction For Beginners’What Is a Neural Network?Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning?we gently explore these topics so that we can be prepared to dive deep further on. To start, we’ll begin with a high-level overview of machine learning and then drill down into the specifics of a neural network.The Math of Neural NetworksOn a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns.* Forward Propagation * Calculating The Total Error * Calculating The Gradients * Updating The WeightsMake Your Own Artificial Neural Network: Hands on ExampleYou will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is…