What Is a Feedforward Neural Network?

Key takeaways

In a feedforward neural network, each node connects to the nodes in the next layer, and data flows forward continuously, with no loops or cycles.

• Professionals who use feedforward neural networks, such as machine learning engineers, machine learning research scientists, and deep learning architects, earn median total salaries between $177,000 and $228,000 per year [1 2 3].

• Two other types of neural networks are convolutional, which have added capabilities for working with images, speech, and video, and recurrent, which are useful for sequencing events and data.

• You can use feedforward neural networks to forecast future values, such as stock market trends; process image or video data, as in self-driving car technology; and advance natural language processing, which is used in developing AI assistants and virtual agents.

Discover more about feedforward neural networks, including what they’re used for, how they work, and the professions that use them. If you’re ready to enhance your AI skill set, enroll in the IBM AI Engineering Professional Certificate. In as little as four months, you can learn about computer vision, machine learning, large language modeling, model evaluation, and more.

What are neural networks?

Neural networks are a tool for deep learning that allows an AI agent to learn from experience and training to determine the best method of accomplishing a task. On a more technical level, a neural network consists of a series of nodes arranged in interconnected layers and assigned weights. When you add data, the neural network filters through the hidden inner layers to produce the output. A feedforward neural network is one where the data passes continuously through the layers, moving from input to output without circling back.

How do neural networks work?

A neural network works by passing data through layers of nodes. Each node interacts with the data in one way and includes a weight to signal how important the node’s interaction is to the final output. The power of the neural network comes not in any individual node, but in the layers and layers of nodes found in deep neural networks. Each node can interact with or change the data in some way, and each layer adds more nodes for the data to interact with. In this way, adding more layers to a neural network allows you to create increasingly complex calculations. 

Read more: Neural Network Weights: A Comprehensive Guide

What is a feedforward neural network? 

The way that data moves through the architecture of the network defines feedforward neural networks. Every neural network starts with an input and results in an output. Between those first and final steps, a neural network has hidden layers that the user can’t see. The hidden layers provide the structure for how the neural network will interact with the data. At the same time, the nature of the system allows artificial intelligence to draw conclusions independent of human intervention.

In a feedforward neural network, each node connects to the next layer's node. The data flows forward constantly, from one layer to another, with no loops or cycles. The data only feeds forward, which is where the neural network got its name. 

What are the other types of neural networks?

In addition to being the simplest and most common form of neural networks, feedforward networks are also the starting point for other types of neural networks, including convolutional and recurrent:

• Convolutional neural networks: A convolutional neural network has added capabilities for working with images, speech, and audio. This neural network includes convolutional and pooling layers between the input and output. These layers allow the AI to detect different properties of images and videos. Each additional convolutional layer enables the AI to understand higher-level patterns. The pooling layer that follows helps aggregate the information back into a usable format.

• Recurrent: Recurrent neural networks can use time-series data and understand sequences of events and data. For example, recurrent neural networks can predict stock market fluctuations or understand how the specific order of words affects their meaning. Another distinguishing factor of a recurrent neural network is that it can take the output and run it back through the algorithm again, an added functionality that feedforward neural networks do not have.

Other types of neural networks include deconvolutional, modular, and generative adversarial networks. Within these categories, neural networks can be further drilled down into more specific subtypes. For example, recurrent neural networks can also be gated recurrent units or long short-term memory neural networks. For convolutional neural networks, you could use the VGG model or a residual neural network. 

What are feedforward neural networks used for?

One prominent reason computer scientists use feedforward neural networks is their ability to approximate functions, which involves making predictions about how to solve problems. Feedforward networks also contribute to other artificial intelligence advancements, such as computer vision, natural language processing, pattern recognition, image recognition, time series prediction, and classification tasks.

Computer vision

This technology allows artificial intelligence to process data found in images or video. In essence, computer vision allows AI to “see” similar to people, filtering visual data through the experience of its training data to draw conclusions and make decisions. Neural network engineers commonly use convolutional neural networks to develop computer vision. A few applications of computer vision include:

• Self-driving cars: Autonomous vehicles use computer vision to understand how to navigate on the road. 

• Content moderation: Computer vision powers AI agents to remove harmful content posted online. 

• Manufacturing: Computer vision can spot defective products before they leave the factory.

• Image search: Computer vision makes it possible to search through images based on keywords depicted in the picture. For example, you could search your Google Photos for images of a specific person’s face.

Can feedforward networks handle rotation of images?

While feedforward neural networks are skilled at detecting objects within images, you will need a feedforward network with specific functionality to manipulate images, such as by rotating them. This is because a basic feedforward neural network can classify images by examining their individual pixels without considering how those pixels relate to one another. Some types of neural networks, such as convolutional neural networks, consider and map the data in a more holistic way to understand how the features relate to one another. 

Natural language processing 

Without NLP, we need programming languages to interact with computers. However, with NLP, computers and other devices can understand human language through text and speech. For example, NLP makes it possible to speak to an AI agent using a natural language you know instead of Python. A few applications of NLP include the following:

• AI assistants: When you speak to your virtual assistant, natural language processing allows it to understand and reply appropriately.

• Virtual agents: You can often chat with an AI customer service agent online when you have a problem with a retailer or other online company.

• Sentiment analysis: Natural language processing allows an AI agent to understand the tone and feeling behind social media posts, giving brands insight into how their marketing efforts are faring.

• Language translation: You can use online tools to translate spoken or written words into a different language. Natural language processing makes this possible and much more accurate than previous machine-learning translations.

Time series forecasting

Time series forecasting is the process of predicting future values based on past data. Since recurrent neural networks can access time series data, they help with time series forecasting. Some of the applications of time series forecasting include:

• Stock market forecasting: Financial institutions and investors can use neural networks to gain insight into stock market trends to inform investment decisions.

• Weather forecasting: Neural networks can help meteorologists understand weather patterns and inform the community about what to expect.

• Retail seasonality: Many companies, like retail stores and restaurants, see their sales fluctuate seasonally. Time series forecasting helps these companies understand how sales fluctuate throughout the year.

Feedforward neural network architecture: How does a feedforward neural network work?

To understand how feedforward neural networks (FNNs) work, it may be helpful to review FNN architecture. Feedforward neural network architecture is the algorithm's structure, the network's number of nodes and layers. In the simplest version of a feedforward neural network, you will find an input layer, a hidden layer with some nodes, and an output layer. Adding more layers can give the network more capabilities for understanding and connecting with the input data.

We’ve modeled neural networks after our brains. They can learn from their experiences and make decisions based on available data. You may wonder, if data constantly flows through a feedforward neural network, how is the deep learning algorithm actually learning?

The answer is backpropagation. This algorithm simulates supervised learning by feeding the output through multiple layers of feedforward neural networks. The algorithm creates the output and calculates the error between the prediction and the result. In response, the algorithm adjusts its weights to help make a more accurate prediction in the future.

Who uses feedforward neural networks?

If you are considering pursuing a career using feedforward neural networks, three potential career titles include artificial intelligence or machine learning engineer, neural network researcher, and deep learning architect.

Let’s take a closer look at each job title and its median total salary based on April 2026 data. 

AI or machine learning engineer

Median annual total salary (Glassdoor): $177,000 [1]

Job outlook (projected growth from 2024 to 2034): 20 percent [4]

Education requirements: Typically requires a bachelor’s degree in computer science or a related field

As an artificial intelligence or machine learning engineer, you will design and create AI algorithms and machine learning models to build and test artificial intelligence systems. In this role, you will often work as a member of a larger team to create an AI or machine learning product. In addition to creating new algorithms and models, you will be responsible for testing your models, performing analyses, and completing documentation.

Machine learning research scientist

Median annual total salary (Glassdoor): $228,000 [2]

Job outlook (projected growth from 2024 to 2034): 20 percent [4]

Education requirements: Commonly requires a master’s degree in machine learning, computer science, robotics, or a related field

As a machine learning research scientist, you will work to create new machine learning algorithms to drive artificial intelligence technology. You could also be working to advance the math behind artificial intelligence. You will collaborate with other professionals in this role, including data scientists and machine learning engineers. 

Deep learning architect

Median annual total salary (Glassdoor): $186,000 [3]

Job outlook (projected growth from 2024 to 2034): 4 percent [5]

Education requirements: Typically requires a bachelor’s degree in computer science or a related field

As a deep learning architect, you will be responsible for designing, building, and scaling your organization’s artificial intelligence infrastructure. You will select and audit the appropriate technologies, tools, and solutions to help your company grow its artificial intelligence offerings at scale. In this role, you will work with other professionals to ensure that every AI decision considers safety and ethics.

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