Neural networks have transformed how machines learn and make decisions, powering everything from voice assistants to image recognition apps. Two of the most important types of neural networks you will come across are Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). In this post, we will break down mlp vs cnn what these networks are, how they work, and why they are so effective.

What is a Multilayer Perceptron (MLP)?

Think of an MLP as a basic neural network made up of three types of layers:

• Input layer: Receives your raw data.
• Hidden layers: Multiple layers of neurons that transform the data.
• Output layer: Produces the final prediction or classification.

Each neuron in the hidden and output layers takes inputs, multiplies them by weights, adds a bias, and then applies a nonlinear activation function. This activation is key it lets the network learn complex patterns that simple linear models can’t.

How Does an MLP Learn?

The learning happens in two main steps:

1. Feedforward: Input data passes through the network layer by layer, getting transformed at each neuron.
2. Backpropagation: The network compares its output to the actual result, calculates the error, and adjusts the weights by moving them in a direction that reduces this error. This is done using gradient descent, a way of minimizing error by following the slope of the loss function.

Through many iterations of this process, the MLP gradually improves its accuracy.

Enter Convolutional Neural Networks (CNNs)

When it comes to images and other grid-like data, CNNs are the go-to architecture. Unlike MLPs, which treat every input feature independently, CNNs are designed to understand spatial relationships like the patterns and shapes in an image.

Core Layers in a CNN:

• Convolutional layers: Apply filters (small matrices) that scan across the input, detecting features like edges, textures, and shapes.
• Pooling layers: Shrink the spatial size of the feature maps, reducing computation and helping the network focus on the most important parts.
• Fully connected layers: Combine all features to classify the input.

The convolution operation can be thought of as sliding a small window (filter) over the image and computing dot products, which highlight important features in the image.

Training CNNs

CNNs also learn through backpropagation and optimization algorithms like stochastic gradient descent (SGD). What’s unique is that the filters themselves are learned, meaning the network discovers which features best help it identify objects during training.

Read More Understanding Prior-Fitted Networks (PFNs) and Bayesian Inference in Deep Learning

Landmark CNN Architectures

Over the years, CNN designs have evolved dramatically. Here are a few that stand out:

• AlexNet: The breakthrough network with five convolutional layers and three dense layers, using innovations like ReLU activations, dropout, and data augmentation to boost performance.
• VGGNet: Known for its simplicity and depth, VGG uses many small (3×3) convolution filters stacked to create deeper networks (like VGG16 and VGG19).
• ResNet: Introduced “skip connections” or residual blocks, allowing the training of extremely deep networks by enabling gradients to flow more effectively during backpropagation.

FAQs

Conclusion

Understanding the difference between MLPs vs CNNs helps you pick the right tool for your data. MLPs are great for general-purpose prediction with structured data, while CNNs excel at image classification and other tasks where spatial features matter.