An Explained and Visual Guide to Neural Networks
15 minutes read · 14 hours ago If you’ve read my previous posts, you already know what to expect. In this section, we take common sense ideas and make them interesting by explaining them. If you haven’t read my previous articles, I recommend starting with my list of articles covering the basics of machine learning as much of that information is relevant here. Today, we’re going to delve into the introduction of Neural Networks, a type of machine learning. This is the first article in a series on Deep Learning. It will look at how simple artificial networks learn and provide a deep understanding of how neural networks are built, neuron by neuron, which is crucial as we continue to develop this knowledge. Although we will delve into the mathematical details, there is no need to worry because we will explain and illustrate each part. By the end of this article, you will realize that it is easier than it sounds. But before we delve into this, you may be wondering: Why do we need neural networks? With many machine learning algorithms available, why choose a neural network? There are many answers to this question, but we will not delve too deeply into them. However, it is worth noting that neural networks are very powerful. They can detect complex patterns in data that older algorithms struggle with, solve machine learning problems (such as natural language processing and image recognition), and reduce the need for deep technical and manual experiments. But with all that said, neural network problems fall into 2 main groups – Category, predicting a special sign of the given content (for example: is this a picture of a cat or a dog? Is the review of this video good or bad?) in other additions (such as: weather forecasting). Today, we will focus on the regression problem. Consider a simple example: we recently moved to a new city and are currently looking for a new home. However, we see that house prices in this area vary widely. Since we are not familiar with the city, our only source of information is what…
