What Is Perceptron?

The perceptron is the simplest architecture within artificial neural networks, making it a great starting point for those unfamiliar with machine learning. By understanding what a perceptron is, its history, and the applications of different types of perceptrons, you can gain a strong foundation in basic deep learning principles, helping you to expand your expertise in this exciting domain. 

What is perceptron? 

A perceptron is the simplest building block of neural networks, used to mimic the way neurons work in the human brain. This algorithm performs binary classification tasks, dividing inputs into one of two categories. You can think of this type of algorithm as a “yes” or “no” decision-maker based on whether the weights of input values reach a certain activation value. 

For example, imagine you are deciding whether to buy a coffee at your local coffee shop. Your “input variables” might be the temperature outside, your weekly budget, and how tired you feel. Each of these conditions has a level of importance or weight that influences your final decision. 

A perceptron takes these inputs and combines their weights to see if they collectively reach a certain threshold. If they do, the perceptron would activate or buy the coffee in this scenario. If not, the perceptron would remain inactive, and you would decide not to buy coffee that day. Over time, the perceptron adjusts the weights of the input layers to improve decision-making. 

History of perceptrons

The perceptron is a foundational concept in machine learning and artificial intelligence, representing an important step in modern neural networks and deep learning advancements. The perceptron was introduced in the late 1950s by psychologist and neurobiologist Frank Rosenblatt. The perceptron was the first machine considered “capable of having an original idea.” It attracted a high volume of media attention around its design and artificial intelligence (AI) as an emerging field.

However, criticism of the perception and its limitations led to a decline in AI funding and research, known as the “AI winter.” It wasn’t until the 1980s that AI regained widespread popularity due to the development of multilayer models, such as the multilayer perceptron. These advances overcame previous limitations, enabling AI to deal with more complex tasks. 

Types of perceptrons

Perceptrons come in different forms, depending on the complexity of your machine-learning task. The most common distinction is single-layer versus multi-layer perceptrons. 

Single-layer perceptron

Single-layer perceptrons have one layer of nodes that directly connect input and output layers. This model learns binary classifiers under supervision, understanding how to interpret input data correctly and assign it to one of two categories correctly. However, single-layer perceptrons can only solve linearly separable problems, meaning they work best when your data can separate naturally into different classifications. 

What are single-layer perceptrons used for?

Common applications of single-layer perceptrons include basic binary classification tasks, like filtering emails (spam/not spam) or signaling whether a gene is expressed in a particular person (yes/no). It’s also popular for educational projects, helping you explore machine learning basics without overcomplicating the setup.

Advantages:

• Simple, easy to implement

• Effective for binary classification

• Provides an introduction to neural network concepts

Disadvantages:

• Limited to linearly separable problems

• Not suitable for multi-class problems

• Unable to capture complex patterns

Multilayer perceptron

Multilayer perceptrons extend single-layer perceptrons by adding hidden layers between the input and the output. This extends the capabilities to more complex problems, like predictions and image pattern recognition. 

What are multilayer perceptrons used for?

Thanks to their hidden layers, multilayer perceptions can solve more sophisticated problems that involve nonlinear classifications and pattern recognition. You can use these models for various health care applications, such as improving disease diagnoses, predicting health risks, and interpreting biological data. Multilayer perceptrons are also used across business and finance professionals to predict stock-market trends, assess company financial health, and assist decision-making.

Advantages:

• Ability to handle complex relationships between variables

• Suitable for multi-class classification

• Able to learn intricate patterns within the data

• Provides quick predictions

Disadvantages:

• Higher computational load

• May be more affected by the quality of training data

• More difficult to implement

Who uses perceptrons?

A variety of professionals use perceptrons in research and industry applications, especially in roles that focus on developing and applying machine learning models. As a machine learning engineer or data scientist, you’ll likely work with perceptrons, as they form the foundational building blocks for more complex neural network architectures. In roles involving the design, training, or implementation of machine learning algorithms, grasping the concept of perceptrons—and the relationship between input, hidden, and output layers—will provide you with a strong foundational knowledge of machine learning.

Start learning perceptrons

Learning perceptrons can be a great starting point for building your neural network and machine learning toolkit. To start, consider the following steps:

1. Learn the basics of machine learning: Understanding basic ideas like supervised and unsupervised learning can help you contextualize more advanced models.

2. Study single-layer perceptrons: As the simplest type of perceptron, this model can help you learn how to combine inputs and weigh them to determine an output.

3. Explore multilayer perceptrons: Once you’ve mastered single-layer perceptrons, you can experiment with more complex problems, adding hidden layers to your model.

4. Practice with online data sets: Finding online data sets or completing Guided Projects, like the ones on Coursera, can help you put your knowledge into action. 

Build your machine learning knowledge on Coursera

Perceptrons are a fundamental building block for artificial neural networks, assigning inputs into binary classifications. As with any new skills, applying it to real-world scenarios can help you build your expertise and identify any areas for improvement. On Coursera, you can work with exciting data sets to explore machine learning in different contexts. Consider starting with the Scikit-Learn For Machine Learning Classification Problems Guided Project to expand your Python knowledge, or check out the Cervical Cancer Risk Prediction Using Machine Learning to gain experience with health care applications.