Understanding the Activation Function in Neural Networks

Written by Coursera Staff • Updated on

Learn about the role of activation functions in neural networks, including the different types of activation functions and how they work.

[Featured Image] An employee sits at a computer and uses the activation function in a neural network to solve complex problems.

Machine learning is a subcategory of artificial intelligence (AI) that relies on algorithms that act similarly to the human brain, progressively learning through data. This learning process gives machines the ability to identify trends and patterns in order to make predictions, automate processes, and assist with decision-making. Neural networks play an important role in machine learning by making machines become more accurate over time as they learn from errors to improve their performance.

Some real-world applications of machine learning powered by neural networks include self-driving vehicles, facial recognition technology, and stock market predictions. Within the layers of neural networks, an essential component known as the activation function helps guide the flow of information through the network, enabling it to learn from the data it receives.

Read more: Machine Learning vs. AI: Differences, Uses, and Benefits

What is a neural network?

Neural networks are essentially algorithms comprising interconnected neurons that transmit information like the human brain. Neural networks receive input data, process it automatically, and produce an output. But how does a neural network know what to do with the data? 

Before a neural network can function independently, it must go through training. During the training process, the neural network learns to identify characteristics within the data. With enough training data, the neural network can then operate on its own to provide accurate results. 

As neural networks receive more data, they do a better job of correctly identifying specific characteristics. For example, if you wanted a neural network to be able to recognize the animals in a picture, you would need to train it with data—in this case, you could use images of different animals with labels that tell the neural network what it’s looking at so it can learn to perform on its own. 

Types of neural networks

You have different options for selecting the type of neural network to use, depending on the application and task you wish it to perform. Here are some common types of neural networks:

  • Convolutional neural network: Convolutional neural networks (CNNs) are commonly used for computer vision tasks. They can classify images based on their unique features, detecting things even the human eye could miss. Other machine-learning applications for CNNs are natural language processing and facial recognition.

  • Feed-forward neural network: The most basic type of neural network, feed-forward neural networks move data through the algorithm linearly and are great for sorting information into classes. Applications for feed-forward neural networks include computer vision and natural language processing. 

  • Recurrent neural network: Rather than moving data through the algorithm linearly, recurrent neural networks can take the outputs they reproduce and use them again as inputs for improved accuracy. Recurrent neural network applications include speech recognition, translation, and sales forecasting.

Neural network architecture

Three regularly occurring neural network components are the input layer, one or more hidden layers, and the output layer. The input layer is where the network receives data and organizes it into categories before it moves on. Next is the hidden layer. Neural networks often have many hidden layers that assign weights and biases to data, allowing the network to turn the data into usable information. Lastly, the output layer presents you with the results.

What is an activation function in neural networks?

The activation function in neural networks determines which neurons should turn on as information moves through the network’s layers. Since the activation function enables non-linear movement of information between neurons within the network, the neural network can learn more about the data it receives. Only the neurons that the activation functions recognize as important to the process will turn on. If the activation function didn’t exist, neural networks would only be able to operate linearly, therefore limiting the amount of data they could process. Instead, the activation function allows the network to recognize complex relationships and patterns within the data.

Types of activation functions

You can group the different types of activation functions into three categories: linear, non-linear, and binary step. Linear activation functions have an output that is directly proportional to the input, and they work by summing the total of the input weights. Non-linear activation functions are the most complex of the three and also the most common. These activation functions enable neural networks to process several types of data. Binary step activation functions, meanwhile, are the simplest type of function. In this case, a threshold value determines the output, and whether a neuron activates will ultimately depend on whether the input it receives is greater than or less than the threshold value. Here are some examples of specific types of activation functions within these categories: 

  • Step function: A step function is an example of a simple binary activation function. In this scenario, a step function assigns the input a zero or one based on the size of the input value. However, due to the information loss that occurs, you won’t often see it in practice.

  • Exponential linear units (ELU): ELU is an activation function that is computationally heavy and therefore requires more time, but exponential linear units are capable of considering negative values and producing negative outputs.

  • Sigmoid activation function: The sigmoid activation takes input data and assigns it a value ranging from zero to one. However, this activation function has challenges when the input value is exceptionally large or small. This is a common choice for non-linear activation functions.

  • Tanh function: Similar to the sigmoid function, the tanh function expands on the output range. Rather than zero to one, the output range is negative one-to-one. This allows you to categorize the output on a scale as positive, neutral, or negative.

Who uses the activation function in neural networks?

Machine learning engineers and data scientists use activation functions to train neural networks when working on artificial intelligence projects, such as those based on machine learning, deep learning, or computer vision. You might also use activation functions in a career as a business intelligence developer to organize data to improve business outcomes for clients or as an applied scientist to develop fraud detection systems. As a software engineer, you would use activation functions in neural networks to create algorithms, organize big data, and develop predictive models. If you're interested in developing technology for chatbots, virtual assistants, translation applications, or related AI projects, you might pursue a career as a deep learning engineer and use your knowledge of activation function in neural networks.

Getting started with Coursera

On Coursera, you can find highly rated courses to explore the topics of machine learning and neural networks. The Machine Learning Specialization from the University of Washington can help you understand classification algorithms, prediction, and clustering in machine learning.

To learn more about neural networks, consider enrolling in Neural Networks and Deep Learning from DeepLearning.AI. This course covers the parameters of neural network architecture as well as how to build and train neural networks. 

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