# Preface

Welcome to our journey into the world of neural networks and Python programming! This book is designed to guide you, step by step, through the process of building a simple neural network from scratch using Python on Google Colab.

Imagine you're an artist, and you've been given a blank canvas, some paint, and a brush. You have the freedom to create anything you want, but you're not sure where to start. This book is like a guide that not only shows you how to hold the brush and mix the paint, but also helps you understand why you're doing it that way.

In the same way, we're going to start with a blank Python notebook, and by the end of this book, you'll have a fully functioning neural network that you've built from scratch. And more importantly, you'll understand why and how it works.

Let's consider an analogy to understand what we're about to do. Imagine you're learning to bake a cake. You could just follow a recipe, step by step, and probably end up with a decent cake. But if you understand why you need to beat the eggs until they're fluffy, or why the oven needs to be preheated, you'll not only be able to make that one cake, but you'll also be able to create your own recipes and troubleshoot when things go wrong.

Similarly, there are many libraries and tools available that can help you build a neural network without understanding the underlying principles. But by building one from scratch, you'll gain a deep understanding of how neural networks work, and you'll be able to build, troubleshoot, and innovate much more effectively.

This book is designed for beginners, so don't worry if you're not an expert in Python or machine learning. We'll start with the basics and build up from there. Each concept will be explained in detail, with plenty of examples and analogies to help you understand.

For instance, when we talk about weights in a neural network, we'll explain it like this: Imagine you're trying to balance a scale. On one side, you have the output of your neural network, and on the other side, you have the actual value you're trying to predict. The weights are like the different objects you can place on the scale to try to balance it. By adjusting the weights, you're adjusting the balance of the scale to get as close as possible to the actual value.

We'll be using Google Colab for our Python programming, which is like a virtual playground for coding. It's a free, online platform that allows you to write and execute Python code right in your browser, without any setup or installation. It's like having a powerful computer at your disposal, no matter where you are or what device you're using.

We'll also be using NumPy, a powerful Python library for numerical computations. Think of it as a super-powered calculator that can handle complex mathematical operations with ease. We'll guide you through the basics of NumPy and show you how to use it to create and manipulate arrays of data.

By the end of this book, you'll have a solid understanding of neural networks and how to program them in Python. You'll be able to create your own datasets, initialize and adjust weights, define and train models, and evaluate their performance.

But more importantly, you'll understand why you're doing each of these steps, and how they contribute to the overall functioning of the neural network. You'll be able to take this knowledge and apply it to more complex models and real-world problems.

So, let's get started on this exciting journey into the world of neural networks and Python programming!