Once you understand the basics of a neural network, the natural next question is why there isn’t just one universal architecture. The honest answer is that the shape of your data determines what structure works best, and that’s exactly what these architectures are each built to exploit.

Convolutional neural networks (CNNs): built for images

CNNs are built around the idea that nearby pixels in an image are related to each other, and that the same small pattern, like an edge or a curve, can show up anywhere in the image. Instead of treating every pixel independently, a CNN slides small filters across the image to detect these local patterns, then builds up from simple patterns (edges) to more complex ones (shapes, then objects) across multiple layers.

• What they’re used for: image classification, object detection, face recognition, and most other computer vision tasks.
• Why they work well for images specifically: they exploit the fact that useful patterns in images are local and repeat across different positions, which a plain fully connected network would learn far less efficiently.

Recurrent neural networks (RNNs): built for sequences

RNNs are built for data where order matters, like a sentence, a time series of stock prices, or audio. Unlike a CNN, an RNN processes input step by step, carrying forward a kind of memory of what it’s already seen, which lets it use earlier context to interpret later input. Long Short-Term Memory networks (LSTMs) are a well known, improved variant of RNNs built specifically to remember information over longer sequences, since basic RNNs struggle with that.

• What they’re used for: text and language tasks, time series forecasting, speech recognition, historically before transformer-based models became dominant for most language tasks.
• Where they stand today: transformers have replaced RNNs for most large-scale language work, but RNNs and LSTMs remain genuinely useful for smaller-scale sequence problems and are still worth understanding conceptually, since they explain a lot about why transformers were designed the way they were.

Generative adversarial networks (GANs): two networks competing

A GAN is built from two networks trained against each other: a generator that tries to create realistic fake data (like an image), and a discriminator that tries to tell real data from the generator’s fakes. As training progresses, the generator gets better at fooling the discriminator, and the discriminator gets better at catching fakes, and the competitive pressure between them is what drives both to improve.

• What they’re used for: generating realistic images, style transfer, and data augmentation, generating extra realistic training examples when you don’t have enough real data.
• Where they stand today: diffusion models have become the dominant approach for most modern image generation tools, but GANs remain a genuinely important concept to understand, both historically and for specific tasks they’re still well suited to.

The best free courses for learning these architectures

• fast.ai’s Practical Deep Learning for Coders: free, code-first, and covers CNNs specifically in real depth through actual hands-on image classification projects rather than pure theory.
• DeepLearning.AI’s Deep Learning Specialization (free to audit on Coursera): covers CNNs, RNNs, and touches on the ideas behind GANs, with Andrew Ng’s characteristically thorough conceptual explanations.
• Transfer learning: a technique worth knowing about specifically alongside these architectures: reusing a model already trained on a large dataset and adapting it to your smaller, specific task, which saves enormous time and works especially well with CNNs.

For a broader path through deep learning specialties, see our guide to free deep learning, NLP and computer vision courses, and for the frameworks you’ll actually build these in, our guide to free TensorFlow and PyTorch courses.

Frequently asked questions