What Is a Feedforward Neural Network?

Of all the types of neural networks, or artificial intelligence (AI) systems that allow computers to process data similarly to human brains, feedforward neural networks are the most common and one of the simplest. 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.

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 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, 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 is a feedforward neural network 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 who can 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.

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 tools online 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 making predictions for the future based on what happened in the past. 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 marketing 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.

How do feedforward neural networks work?

To understand how feedforward neural networks 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 their average base salary based on January 2024 data. 

AI or machine learning engineer

Average annual base salary (Glassdoor): $131,223 [1]

Job outlook (projected growth from 2022 to 2032): 23 percent [2]

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

Average annual base salary (Glassdoor): $149,263 [3]

Job outlook (projected growth from 2022 to 2032): 23 percent [2]

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

Average annual base salary (Glassdoor): $134,814 [4]

Job outlook (projected growth from 2022 to 2032): 8 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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If you’d like to learn more about feedforward neural networks, consider taking the next step and earning your IBM AI Engineering Professional Certificate. This six-course series takes approximately two months to complete, in which you'll have the opportunity to develop skills in AI, deep learning, computer vision, neural networks, and more.