What Are Neural Networks?

Neural networks play an important role in artificial intelligence and machine learning by training computers to process the data they receive. These artificial networks follow your brain's learning process—by sharing information across a network of neurons. Like your brain, this process allows neural networks to become better at solving problems, continuously improving as the neural networks receive more information—often through trial and error.

Neural networks have practical applications in various industries, including finance, health care, and technologies such as facial recognition. According to the World Economic Forum's 2023 Job Report, jobs related to neural networks are among the fastest-growing careers in the world.

What are neural networks?

Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data. By using algorithms, neural networks can learn without someone to reprogram the model. As a result, neural networks adapt to become increasingly accurate as they receive more training data from which to learn. 

The interconnected neurons, or nodes, that work together to form a neural network consist of three layers: the input layer, the hidden layer (or layers, as sometimes a neural network has multiple hidden layers), and the output layer. Neural networks also have adjustable weights and biases that evaluate the significance of the inputs and activation functions to decipher the relationship shared between the inputs and outputs.

Types of neural networks

Different types of neural networks exist, each with its advantages and purpose depending on the structure of your data and the problem you’re trying to solve. Three common neural networks include convolutional, feedforward, and recurrent.

1. Convolutional neural network

Convolutional neural networks (CNNs) are commonly used for computer vision and image recognition. This type of neural network has multiple hidden layers with filters that analyze specific features of the image and classify them for future reference. CNNs identify patterns found in images using mathematical functions such as matrix multiplication.

2. Feedforward neural network

In a feedforward neural network, data moves in a single direction across nodes—from input to output. When looking at the structure of multilayer perceptrons (MLPs), you will see that the perceptrons (neurons) connect directly to each perceptron in the proceeding layer, which helps facilitate data flow. You may see this type of neural network used in natural language processing as well as computer vision.

3. Recurrent neural network

Recurrent neural networks (RNNs) have feedback loops, and this structure allows them to take the previously delivered outputs and use them again as inputs. You'll find (RNNs) in speech recognition and sentiment classification features. They also have the ability to recall the formerly processed information and use it to make predictions of future outcomes.

What are neural networks used for?

Several industries utilize neural networks, including manufacturing, electronics, telecommunications, automotive, and robotics. Researchers and developers in these industries are using neural networks in exciting ways, as you see in the following list of applications:

• Diagnosing diseases

• Predicting stock market performance

• Detecting fraud

• Supporting voice recognition software

• Powering flight path simulations

• Detecting failure in aircraft components

• Translating languages in real-time

• Inspecting machinery and products for quality

• Forecasting sales

• Analyzing social media behavior for targeted marketing

Pros and cons of neural networks

Researchers started working with artificial neural networks in the mid-twentieth century, and they have since uncovered distinct advantages and disadvantages for use within AI and machine learning. The following list highlights some advantages of working with neural networks:

• They can help you solve complex problems, in part because they can learn from previous examples and the output they produce.

• If you assign more than one task, the network can process all of the data without reducing performance.

• Data used by neural networks stays within the network, so they're unaffected by data loss. 

However, challenges do exist with neural networks. Researchers continue looking for solutions to the following limitations:

• You may have difficulty seeing why the network produced the given output, which means you may not be able to find the source of incorrect output immediately.

• Researchers also have concerns about the stability of neural networks, noting how much they depend on high-quality data for optimum performance.

• Additionally, developing neural networks often costs a significant amount of time, money, and data.

Careers that use neural networks

If you’re interested in pursuing a career working with neural networks, you have several options, including a number of roles in artificial intelligence and machine learning. Here are examples of jobs that may use neural networks.

1. Data engineer

Average annual salary (US): $106,994 [1]

Data engineers build data pipelines that enable people within their organization to access information. This involves collecting data from different sources, developing automation scripts, and creating algorithms. In this role, you may rely on neural networks to process large amounts of data a company or organization collects.

2. Machine learning engineer

Average annual salary (US): $124,670 [2]

Machine learning engineers develop software that supports machine learning applications, often including neural networks. They often help program the algorithms and machine learning code for areas such as natural language processing. In this position, you may document artificial intelligence and machine learning processes for others to understand.

3. Research scientist

Average annual salary (US): $116,268 [3]

As a research scientist, you may specialize in AI or machine learning, which creates opportunities to work with neural networks. In your work, you may research solutions for current problems affecting computer hardware and software, look for better ways to clean data for use in neural networks or look for answers to questions being raised within the AI and machine learning communities.

Getting started with Coursera

On Coursera, you can find highly rated specializations to learn more about neural networks. With Stanford's Deep Learning Specialization you can build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods.