In the exciting world of artificial intelligence and deep learning, the Multilayer Perceptron (MLP) stands as the simplest and fundamental deep learning model—a powerful tool for solving complex problems across various domains. In this article, we will delve into the basics of a Perceptron, understand its functioning, explore the limitations of a single-layer Perceptron, and then dive into the world of Multilayer Perceptron (MLP) with its architecture, components, training algorithms, and applications.
What is MultiLayer Perceptron(MLP)?
The Multilayer Perceptron (MLP) is a fundamental Deep Learning model based on the perceptron concept and the feedforward neural network (FNN) architecture, where every neuron is connected to the neurons in the next layer, and the input data flows in the forward direction. In other words, MultiLayer Perceptron is a type of FNN architecture that uses the perceptron concept with multiple hidden layers to increase complexity and improve accuracy.
But why is it called a Multilayer Perceptron, and what does ‘perceptron’ mean? To answer this, let’s first explore the concept of a perceptron.
What is a perceptron?
The perceptron is a conceptual model and a mathematical representation of the function of biological neurons. This was first introduced by Frank Rosenblatt at the Cornell Aeronautical Laboratory in 1957. This is the foundational building block of neural network concepts.
In this process, the perceptron takes multiple inputs, multiplies them by their respective weights, sums them up, and passes the result through an activation function to determine the output.
The original perceptron model uses a simple step function (often called the Heaviside step function) as its activation function. However, this activation function is not sufficient to capture non-linear relationships.
If you’re unfamiliar with mathematical computations and how neural networks make predictions, you can refer to our comprehensive guide on Artificial Neural Networks (ANN) for a deeper understanding.
Training a Perceptron involves adjusting the weights and the bias to minimize the error between the predicted output and the desired output. This process is known as the training phase. A training algorithm, such as the perceptron learning rule or the delta rule, is employed to update the weights incrementally and iteratively until convergence is achieved.
For a better understanding of neural network training process, follow our guides on Forward Propagation and Backpropagation.
This perceptron model can only handle basic supervised regression or binary classification due to its singular neuron, which is insufficient for capturing nonlinear and deep patterns and relationships in data.
How does multi-layer perceptron work?
To overcome the limitations of the perceptron, when building a neural network, we increase the number of neurons in the hidden layers. In other words, instead of using a single neuron, we expand upon the perceptron concept by employing multiple neurons. This is what we refer to as a neural network. Since both the perceptron and this neural network structure handle data that only flows in a forward direction, we define this type of neural network as a Feedforward Neural Network (FNN).
Here each neuron in the hidden layer represents different features within the data. The number of nodes and layers in a multi-layer perceptron (MLP) depends on the complexity of the problem being addressed.
Also, we use modern Activation Functions like ReLU, tanh, or sigmoid to introduce the non-linearity into the model. This enhances the AI model’s accuracy by capturing non-linear relationships and acquiring more information from the data.
But to capture deeper features in data, such as images, one hidden layer is not enough, so we add multiple hidden layers to the neural network.
This increases the depth of the neural network, which is why we refer to it as a deep learning model. Since the main building block and fundamental concept used here is the perceptron, we specifically call this deep learning approach a MultiLayer Perceptron (MLP)
In a multilayer perceptron model, the first hidden layers may capture basic features, while the deeper layers can capture more complex features, allowing the model to more accurately identify deeper relationships and patterns.
For example, let’s consider a multilayer perceptron deep learning model like the one below, designed to identify digits in given images.
In this multilayer perceptron model, we can assume that the neurons in the first hidden layer represent the small lines of the digits used during the training of the model.
The neurons in the second layer may represent the different shapes that can be created by the small lines.
Therefore, the hidden layers in the MLP model may capture features associated with digits like this:
To gain a better understanding, let’s see what happens when we input an image of the digit number 2 into the trained MLP model.
If our MLP model is well-trained, then the activations and predictions of its neurons would resemble something like the example above.
To learn how to build a deep learning multilayer perceptron (MLP) model for digit recognition similar to the one shown above using PyTorch, follow our article ‘First-AI‘
Application of multi-layer perceptron
Multilayer Perceptron (MLP) is a deep learning model that is used in various domains. Here are some common applications of MLP:
- Image and Speech Recognition: MLPs are widely used in tasks like image and speech recognition tasks. They can process large sets of features and recognize patterns within data, making them suitable for tasks like face recognition, object detection, and speech-to-text conversion.
- Natural Language Processing (NLP): MLPs are used in NLP for tasks such as sentiment analysis, text classification, and language translation.
- Anomaly Detection: In fields like cybersecurity, finance, and industrial equipment monitoring, MLPs can be utilized for detecting anomalies by learning to recognize unusual patterns or behaviors in large datasets.
- Financial Forecasting: MLPs are commonly used for analyzing time series data such as financial market trends. They can effectively identify non-linear relationships and predict future values of stocks, exchange rates, and more.
- Recommendation Systems: In e-commerce and content recommendation systems, MLPs are used to personalize recommendations based on user behavior and preferences. They can help improve user engagement and retention.
- Healthcare: MLPs are commonly used in healthcare for various tasks, such as disease diagnosis, medical image analysis, and predicting patient risks. They can efficiently process complex medical data, including images, time series data, and electronic health records.
- Autonomous Vehicles: MLPs are part of the neural networks used in autonomous vehicles for tasks like object detection, lane tracking, and decision-making.
However, MLPs may not be the best choice for all scenarios. Other neural network architectures like Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data, have their own strengths and are better suited for specific tasks.
Multilayer Perceptron (MLP) is a powerful neural network architecture that can model complex relationships and solve a wide range of problems. By extending the capabilities of the single-layer Perceptron, MLPs have become an essential tool in the field of artificial intelligence and machine learning. Throughout this article I gave you a solid understanding of MLP basics, architecture, training algorithms, and limitations, offering a strong foundation for further exploration and utilization of this versatile neural network architecture.