What Are Neural Networks? A Beginner’s Complete Guide

In the last few years alone, advancements in artificial intelligence (AI) technologies have disrupted, for better or worse, how our world operates and our roles within in it.  

While these breakthroughs have sparked plenty of mainstream debate, especially following the introduction of Chat GPT in 2023, the inner workings of AI systems still elude most people. And with good reason – they’re complicated. One AI that is simultaneously everywhere but little understood is the artificial neural network (ANN), a subset of machine learning AI inspired by the processes in the brain. Given their prevalence, what are neural networks, anyway, and how do they work?  

Here at Pricefx, as a pricing software provider, we’re big advocates of AI (in fact, we’ve just added Gen AI to our roadmap in early 2024). To reap the full benefits of advanced AI technologies like neural networks, having a good grasp of their basic components – from their inner mechanics to applications in the real world – is fundamental, and we’re here to support that understanding.  

In this complete guide for beginners, we’ll break down what neural networks are, including how they work and where they’re used, and offer some key takeaways for using neural networks for business in the future.  

So, let’s dive in.  

What’s a Neural Network?  

Neural networks, or more appropriately, artificial neural networks, refer to a machine learning method in AI consisting of several layers of nodes, or artificial neurons. Using algorithms, neural networks learn from data over time to identify patterns, eventually drawing more accurate conclusions from new data as they improve.  

Its name, introduced in 1944 by Warren McCullough and Walter Pitts, comes from its likeness to human thinking processes, with its structure inspired by the network of neurons found in the advanced brain. 

Neural networks can solve problems that simpler algorithms can’t, but come naturally to humans, such as identifying faces and objects in images and videos, or making sense of and replicating natural (i.e., human) language.  

After an extensive training period, these systems can go on to make inferences from data without our explicit instructions. For example, after being exposed to enough examples, a neural network-enabled virtual assistant like Amazon Alexa at some point recognizes that a question phrased in various ways refers to the same thing, for example, categorizing “how do I get to the airport?” and “transportation options to the airport near me” as the same request.   

Right now, neural network capabilities generally fall into one of these categories: computer vision (detecting and interpreting visual data), speech recognition (converting human speech to text), and natural language processing (understanding human language), and recommendation systems (suggesting tailored options). 

What a Neural Network Isn’t: An Artificial Human Brain 

The term neural networks itself can be a bit misleading, contributing to a popular misconception that these systems can “think” on their own. While neural networks loosely model the human brain, they don’t mimic human thought. Artificial neural networks are learning, not thinking, machines, and still rely on training data from humans to complete their tasks.  

How Do Neural Networks Work? 

Basic Architecture of Neural Networks  

The neural network system’s basic structure can be broken down into three parts: an input layer, a hidden layer, and an output layer: 

Input Layer: The input layer is the point of entry for all training data into the neural network system and contains the input fields.  

Hidden Layer:  The hidden layer sits between input and output layers and isn’t directly visible. As the network’s computational center, the hidden layer is where data from the input layer is analyzed, categorized, and transformed for the output layer. The more hidden layers in a neural network, the “deeper” that network is.  

Output Layer: The output layer is the last layer of a neural network and produces the final predicted result, and, depending on the kind of task the network is working on, multiple results are possible.  

Underlying Mechanics of a Basic Neural Network 

While a bit overwhelming to take in at first glance, when broken down, a traditional neural network is in large part made up of dozens of simpler equations talking to each other, passing off data in a forward motion.  

To break this down further, a neural network system typically consists of several layers of nodes. Each node has its own activation function, and, in the simplest systems, that can take the form of a linear regression equation. Between nodes is a weighted connection, an indication of the degree of influence one node has on the other, that push data in the right sequence.  

For example, consider a neural network for a personalized recipe generator. The first layer of nodes could correspond to a user’s diet preferences. One node asks if a user eats meat, while others determine whether the user is vegan or vegetarian. If a user likes meat-heavy food, the weights connecting the nodes for Diet Preferences to the nodes representing meat-based recipes would be strong. On the hand, for vegetarian or vegan users, the weights between those same nodes would be weak or negative. 

So, in fact, a basic neural network isn’t structurally too complicated. However, more advanced models, namely deep learning neural networks, are made up of a remarkably varied set of functions and algorithms that extend far beyond linear regression equations.  

Simple Neural Networks vs. Deep Learning Systems  

You might have heard neural network and deep learning used interchangeably in the past, but they aren’t quite the same thing.  

As we know, a neural network is a machine learning method consisting of interconnected layers of nodes. A deep learning system, on the other hand, is a highly complex neural network with multiple hidden layers. In other words, deep learning systems are advanced versions of the classic neural network from the mid-20th century, and most neural networks as we know them today are deep learning neural networks.  

Simple neural networks and deep neural networks differ in important ways, including: 

• Depth: Simple neural network systems have just one hidden layer, while deep learning systems have at least two up to thousands. 
• Types:  While a simple neural network is typically the feed-forward type, meaning the data only travels in one direction, deep neural networks offer more flexibility in how the data moves around and is processed by the system, taking the form of other types like recurrent (RNN) and recursive (RvNN)  neural networks.  
• Training Data Volumes: While deep learning systems require upwards of millions of data points for training purposes, simple neural networks need hundreds or thousands.  
• Cost: To accommodate massive amounts of training data, deep neural networks require more expensive hardware and significantly more memory and processing power than traditional neural networks do. 
• Implementation: Due to the complexity of the data sets in training and consequently a longer learning period, deep learning systems usually take longer to develop and set up than traditional neural networks.  

Deep learning neural networks can be found in many industries and for many purposes, like visual and speech recognition, natural language processing, recommendation engines, weather forecasts, and health care. Chat GPT itself relies on Large Language Models (LLM), which are deep RNN models, to produce convincing responses to user questions on a wide range of topics.  

How Neural Networks Are Used in the Real World: Common Uses 

From ChatGPT to Spotify, Amazon Alexa to Uber, neural networks quietly run in the background of our daily lives in more ways than we realize. Today, some of the most common applications of neural networks include: 

• Process and quality control, supporting higher production and safety standards in machinery-reliant industries like discrete and process manufacturing by identifying irregularities and suggesting future improvements. 
• Personalized recommendations, predicting what a user might like based on their historical buying decisions and web activity, such as personalized music playlists or suggested product groupings on e-commerce sites. 
• Price optimization, in which prices are dynamically adjusted based on an analysis of market trends, competitor pricing, and historical pricing data that helps ensure profitability and market competitiveness. 
• Targeted ads & content to support marketing campaigns, tapping into users’ behavioral data, buying history, and demographic information to suggest content or products that are likely to be engaged with.  
• Medical diagnosis, supporting medical facilities with interpreting and categorizing medical images and other complex clinical data to detect traces of illness or disease.  

Neural networks are employed by industry leaders across diverse industries, most visibly by tech giants. Few go into specifics, but Open AI continues to pioneer deep learning AI technologies that exhibit what they call “human-level performance”, Amazon stated it uses deep learning neural networks to forecast daily demand for its 400+ million products, and many others should follow suit. 

Considerations for Using Neural Networks in Business 

If you’re in a company thinking about using neural networks in its business operations, it’s vital to recognize what that decision would require of you and what to reasonably expect.   

Consider the current state of your company’s data. In aggregate, is your historical data representative of the (improved) outcomes you’re hoping to achieve with a neural network-enabled solution? The neural network trains on your company’s data, and you won’t want it replicating the same old logic that gave way to subpar results in the past. If it isn’t representative, ensure that the data is placed in the right context in your initial instructions to ensure the system is aligned with your goals. And even if it is, that data should be in good condition to enable the neural network to learn effectively and come up with accurate insights.   

Neural networks are also costly to build; the more parameters considered in the model, the more expensive they will be, particularly in the memory bandwidth needed to store them.  And in case you aren’t starting from scratch, keep in mind that gathering and cleaning millions of data points for neural network-enabled solutions is typically a time-intensive and costly project too.  

Lastly, think about how comfortable your organization is with accepting conclusions from a system with a decision-making logic that is, for the most part, inaccessible. Such is the nature of the black box, or opaque, systems, that define most neural networks out there today (although, several experiments on neural networks are underway to enable more interpretability of their results). Your answer to this question will in large part determine the long-term reliability of a neural networks-enabled solution for your company.  

Is Neural Network-Driven Pricing Right for You? 

In this article, we took you through the basics of neural network systems – what they are, how they work, and where they’re used – and left you with a few key considerations to keep in mind at your company before diving in.  

Have we left you curious about implementing neural networks in your pricing? Consider heading to our in-depth exploration of the implications of using neural networks for pricing AI optimization: