Unraveling Neural Network Models: A Comprehensive Guide

Key takeaways

Neural network models are artificial intelligence programs that empower computers to make decisions without human intervention. At a glance, here's what you need to know about neural network models:

• The design of neural networks are inspired by the human brain, which feature a vast web of neurons that allow them to process information.

• Through a series of predictions and optimization, neural network models can increase its accuracy over time through training. 

• Common types of neural network models include feedforward neural networks, recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep neural networks (DNNs).

Below, you'll learn more about neural network models, including how they work and how professionals in many industries use them. Afterward, if you want to learn more about neural networks, you might consider enrolling in DeepLearning.AI's Deep Learning Specialization.

What are neural network models?

Neural network models, sometimes separated into categories of artificial neural network (ANN) models and simulated neural network (SNN) models, are artificial intelligence programs that enable an AI model to make intelligent decisions based on the data you present. These programs are structured differently depending on the type of neural network model you use, but you’ll find that all neural networks have an input layer, an output layer, and at least one hidden layer in between. Every hidden layer inside the network has nodes that receive data, manipulate the data, and send the data to the next layer.

Neural network models can have many, many layers, helping the model make increasingly more complicated decisions for you and understand more complex data. This creates a type of learning called deep learning, an advanced AI technique using neural networks with many layers. 

How do neural network models work?

Neural network models can process complex information and make decisions using nodes and a system of strengthening and diminishing the connections between those nodes. Each node connects to other nodes in layers before and after it, and each of these connections has a corresponding weight, which represents how important or relevant that information is. As the network learns, it will change these weights to find a more accurate final output. 

For example, imagine that you'd like to create a neural network to choose the best restaurant in a city, helping you select a place to eat while you’re on vacation. You would enter the list of restaurants as the input layer. In the first layer, the neural network might check to make sure these restaurants are all still in business and open on the day you’d like to stop by. Sending only the restaurants that meet the initial criteria onto the next layer, the neural network might then decide on an average price per plate, weeding out restaurants over your budget and using weights to rank the restaurants that most closely align with your price range.

The next layer might look at what type of restaurant you want to eat at, weighting restaurants that serve your favorite foods higher. Depending on how complex you want your network to be, the layers could continue making more specific decisions to help you arrive at the final output: a ranked list of the restaurants the AI model thinks you will like the best. 

If you told the AI model which restaurant you ultimately picked, it could learn to be more accurate next time by changing the weight system between nodes. For example, if you decide on a restaurant that serves your favorite food but is a little over your budget, the neural network model could change its weight to allow the type of food to be more important than the price. This process of adjusting and optimizing weights is the basis for machine learning. 

Read more: How Do Neural Networks Work? Your 2026 Guide

What are neural network models used for? 

Neural networks can look for patterns and relationships within complicated data sets to make predictions. You can adapt this technology to use in many different circumstances: 

• Detecting financial fraud or cybercrime

• Powering computer vision that enables self-driving cars, improved medical imaging, and facial recognition

• Forecasting energy demand and the needs of the electrical grid or other utility systems

• Predicting maintenance on industrial equipment and performing quality control

• Using natural language processing to understand verbal or written language

• Creating targeted or personalized marketing campaigns

Types of neural network models

You can use different types of neural network models to accomplish different tasks. Explore three types of neural network models—feedforward, recurrent, and convolutional—and learn how to use neural network models to create a deep neural network. 

1. Feedforward neural networks

Feedforward neural network models are so-called because they feed forward; that is, the data moves from one layer to another between the input and the output without looping back through any layers. You can use feedback to train these models, similar to the example above where you told the AI model which restaurant you selected. When you provide feedback to a feedforward neural network, it can adjust its weights and improve its accuracy based on its previous error rates. This process is called backpropagation. 

2. Recurrent neural networks (RNNs)

Recurrent neural networks feature feedback loops, allowing the nodes to remember data it processed in the past. This feature means that the nodes can compare current input against past inputs and make a prediction using both numbers. Feedback loops make recurrent neural networks a great tool if you’d like to work with time series or sequential data, such as the past performance of the stock market or the order in which words appear in a sentence. Using historical data, an RNN can predict what might happen in the future. 

3. Convolutional neural networks (CNNs)

Convolutional neural networks use additional layers, which makes them well-suited for image and pattern recognition. After the input, CNNs send data to one or more convolutional layers, which detect different features of an image, such as its edge or objects in the image. Next, the data goes to a pooling layer that simplifies the image, reducing complexity but making it easier for the neural network to work with. Last, the data arrives in the fully connected layer where the AI model classifies the image. Although this type of neural network is often used for image classification, you can also employ it for natural language processing and other technology. 

4. Deep neural networks (DNNs)

A deep neural network is not a type of neural network model but rather a way to describe neural networks with more than three layers. In contrast, a basic neural network has an input, one hidden layer, and an output. The more hidden layers you add within a deep neural network, the more functionality you add, allowing the network to understand and manipulate the input in new ways. Considering the earlier example about creating an AI model to choose a restaurant, you could add additional layers for every other point of consideration you want your AI model to factor into its calculations. The more hidden layers you add to a deep learning model, the more complex it becomes.

Read more: 8 Common Types of Neural Networks

Who uses neural network models?

Professionals in industries like life and health sciences, manufacturing, financial services, and retail use neural network models. If you’re considering a career that involves working with neural network models, explore these three potential options: AI research scientist, data engineer, and deep learning engineer. 

1. AI research scientist

Median total pay in the US (Glassdoor): $204,000 [1]

Job outlook (projected growth from 2024 to 2034): 20 percent [2]

As an AI research scientist, you will use the scientific process to look for and discover new ways to work with AI technology. In this role, you may focus more on the theory of AI algorithms or on more practical applications using this technology. You may publish research papers with your findings, apply your work to product development, or help a company shape its technical processes. 

2. Data engineer

Median total pay in the US (Glassdoor): $133,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [4]

As a data engineer, you will create and build systems that collect and distribute data to where it’s needed. For example, you might write a program that extracts marketing data from online activity so marketing professionals can use this insight to inform their marketing strategy. In this role, you will work with other professionals to build and maintain data pipelines. 

3. Deep learning engineer

Median total pay in the US (Glassdoor): $152,000 [5]

Job outlook (projected growth from 2024 to 2034): 34 percent [4]

As a deep learning or machine learning engineer, you will build and train neural network models and other forms of machine learning to solve problems using AI. You will select and load training data into machine learning models, optimize the programs for best performance, and identify common machine learning problems like overfitting and underfitting. 

Learn more about neural network models on Coursera

Neural network models allows computers and machines to make intelligent decisions based on complex data. Learn more about AI and its applications with these resources from Coursera:

• Watch on YouTube: Machine Learning in Real Life: From Spotify to Healthcare

• Compare programs: Which Introduction AI Is Best for You? Google vs. IBM vs DeepLearning.AI

• Explore: AI for Professionals Collection

With Coursera Plus, you can learn and earn credentials at your own pace from over 350 leading companies and universities. With a monthly or annual subscription, you’ll gain access to over 10,000 programs—just check the course page to confirm your selection is included.  

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial