What Are Neural Networks?

Key takeaways

Neural networks are computational models that learn how to recognize patterns, develop predictions, and process data.

• Researchers and developers use neural networks to detect failure in aircraft components, forecast stock market performance, identify diseases, inspect products for quality, and more. 

• The three common types of neural networks are convolutional, feedforward, and recurrent.

• You can build or implement neural networks in roles such as data engineer, machine learning engineer, and research scientist. 

Find out what a neural network is, including its various types, key benefits and potential limitations, and professions that utilize them. Afterward, if you’re ready to learn how to build and train neural networks, enroll in the Deep Learning Specialization. Offered by DeepLearning.AI, this program also covers computer vision, supervised learning, large language modeling, and image analysis. 

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.

Read more: Deep Learning vs. Neural Network: What’s the Difference?

Is ChatGPT a neural network?

ChatGPT is a large language model (LLM) that is underpinned by a neural network called “transformer”. With attention mechanisms built into its transformer architecture, ChatGPT focuses on the most significant parts of input text, enabling it to process large volumes of data. The model learns from an extensive database of text, including books and articles, to interpret context and meaning.

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 artificial intelligence (AI) and machine learning (ML). 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 the 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 AI and ML. Here are examples of jobs that may use neural networks.

1. Data engineer

Average annual base salary (US): $108,000 [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 that a company or organization collects.

2. Machine learning engineer

Average annual base salary (US): $126,000 [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 AI and ML processes for others to understand.

3. Research scientist

Average annual base salary (US): $151,000 [3]

As a research scientist, you may specialize in AI or ML, 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 ML communities.

Explore helpful resources for your career journey

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• Learn the terminology: Artificial Intelligence Glossary: Learn AI Vocabulary

• Watch on YouTube: Decision Tree Machine Learning Explained Simply

• Get certified: 6 Machine Learning Certificates + How to Choose the Right One for You

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