Illustrated Deep Learning, Part 1: How Does a Neural Network Work? | By Shreya Rao | January 2024

An illustrated and intuitive guide to neural networks

15 minute read

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13 hours ago

If you've read my previous articles, you'll know what's coming next. In this part of the Internet, we take complex concepts and make them fun and interesting by clarifying them. If you have not read my previous articles, I highly recommend you start with the series of articles covering the topic Machine learning basics Because you will find that much of the material covered there is relevant here.

Today, we'll be covering the big topic – an introduction to neural networks, which are a type of machine learning model. This is just the first article in a whole series I plan to do on deep learning. It will focus on how a simple artificial neural network can learn and provide you with information deep (Ha, pun) Understanding how a neural network is built, neuron by neuron, is… excellent Essential because we will continue to build on this knowledge. While we'll delve into the mathematical details, don't worry because we'll break down and explain each step. By the end of this article, you will realize that it is simpler than it seems.

But before we explore that, you might be wondering: why do we need neural networks? With so many machine learning algorithms available, why choose neural networks? The answers to this question are many It has been widely discussedSo we won't delve into it. But it is worth noting that neural networks are incredibly powerful. They can identify complex patterns in data that classical algorithms would encounter, address very complex machine learning problems (such as natural language processing and image recognition), and reduce the need for extensive feature engineering and manual efforts.

But all that said, neural network problems largely boil down to two main categories – classification, predicting a discrete label for a given input (eg: Is this a picture of a cat or a dog? Is this movie review positive or negative?) or regression, predicting the value of Continuous for a given input (eg: weather forecast).

Today we will focus on the regression problem. Consider a simple scenario: We recently moved to a new city and are currently looking for a new home. However, we note that house prices in the region vary greatly.

Since we do not know the city, our only source of information is what we know…