Every neural network starts from the same idea: neurons arranged in layers, weights that get adjusted through training, a prediction that comes out the other end. LegacyVia’s guide to how neural networks work walks through that foundation piece by piece, using a single artificial neuron as the starting point.
But once you understand that foundation, a fair question shows up: if every neural network works the same basic way, why do we need CNNs, RNNs, GANs and transformers at all? Why not just build one kind and use it for everything?
The answer is that the basic mechanics stay the same, but different types of data reward different architectures. A photo, a sentence and a stock price all behave differently and the network that’s great at one is often mediocre at another. This guide goes through the main types of neural networks in use today, what each one is actually built to do and where you’ve probably already met them without realizing it.
Key Takeaways
- A “type” of neural network isn’t a different technology it is the same neuron-and-layer structure, arranged differently to fit a specific kind of data.
- CNNs are built for images and anything laid out on a grid. RNNs and LSTMs were built for sequences, like text or time-series data, where order matters.
- Transformers replaced RNNs for most language tasks after 2017 because they can look at an entire sequence at once instead of one step at a time.
- GANs pit two networks against each other one generating fake data, one trying to catch it and the competition is what makes the output realistic.
- Most real AI products combine several of these types. A single app like a photo-editing tool might use a CNN to detect your face and a GAN or diffusion model to edit the background.
- You don’t need to memorize every architecture to use AI well. Knowing what each type is for is usually enough to understand why a given tool works the way it does.
Why Neural Networks Come in Different Types
Picture three completely different problems: recognizing a face in a photo, predicting the next word in a sentence, and generating a realistic image from a text prompt. All three could technically be handed to the same basic type of network the plain, fully-connected structure described in LegacyVia’s neural networks guide and it would even work, sort of. But it would be slow, it would need an unreasonable amount of data, and it would ignore obvious structure in the problem that a smarter design could take advantage of.
An image has spatial structure, a pixel’s neighbors matter. A sentence has sequential structure word order changes meaning. A generative task needs a way to judge whether its own output looks real. Each specialized architecture below is really just the same core neuron-and-weight idea, reshaped to exploit one of those structures instead of ignoring it.
Feedforward Neural Networks: The Starting Point
The feedforward network (FNN) is the simplest type, and it’s the one already covered step by step in LegacyVia’s guide to how neural networks work data enters through an input layer, passes through one or more hidden layers, and produces an output, with no loops and no memory of anything that came before.
Feedforward networks are still genuinely useful today. They handle structured, tabular data well the kind of spreadsheet-shaped information used in credit scoring or basic demand forecasting and they’re the architecture every other type on this list builds on top of. Once you understand a feedforward network, every architecture below is really a variation on the same theme, with one added trick.
Convolutional Neural Networks (CNN): Built for Images
A CNN is what you get when you design a network specifically to notice spatial patterns the kind of structure that shows up in photos, video frames and anything else laid out on a grid.
Instead of feeding every pixel into the network as an unordered number, a CNN slides a small filter (sometimes called a kernel) across the image, a few pixels at a time. That filter is only looking for one thing maybe a diagonal edge, maybe a patch of a particular color and it produces a “feature map” showing where in the image that pattern shows up.
Here’s the part that makes CNNs powerful: this happens in layers, and each layer builds on the last one.
- Early layers detect simple things edges, corners, patches of color.
- A pooling step between layers shrinks the feature map down, keeping the strongest signals and discarding the rest. This keeps the network fast and makes it less sensitive to small shifts, like a face being slightly off-center.
- Middle layers combine those edges into shapes, a curve, a corner, an outline.
- Later layers combine shapes into objects, an eye, a wheel, a street sign.
- A final, fully-connected layer takes all of that and turns it into a decision: “cat,” “dog,” “stop sign,” or a probability score for each possible category.
This is the same edges-to-shapes-to-objects idea already introduced in LegacyVia’s neural networks guide a CNN is just the specific architecture built to do it efficiently on grid-shaped data.
CNNs are the reason your phone can unlock by recognizing your face in different lighting, the reason a radiologist can get a second opinion from a model trained to spot early signs of cancer in a scan, and the reason a self-driving car’s camera can tell a pedestrian from a mailbox in a fraction of a second. According to a 2026 market report from SNS Insider, the AI-driven computer vision market is projected to grow to over $342 billion by 2035 a scale that reflects how much of modern AI depends on this one architecture.
Recurrent Neural Networks (RNN) and LSTMs: Built for Sequences
Images don’t have an inherent order you can look at a photo starting from any corner. Language isn’t like that. “The dog bit the man” and “the man bit the dog” use the same words, but order changes everything.
A recurrent neural network was designed to handle exactly that kind of data text, speech, sensor readings, stock prices anything where the sequence matters. Instead of processing all the input at once, an RNN works through it step by step, and after each step, it carries a “hidden state” forward a kind of running memory of everything it has seen so far.
That sounds like a clean solution, and it mostly is, but plain RNNs run into a real problem over long sequences: information from early in the sequence tends to fade out by the time the network reaches the end, a well-documented issue known as the vanishing gradient problem. By the time an RNN reaches the tenth word in a long sentence, it may have all but forgotten the first one.
The fix, developed in the 1990s, is the LSTM — the Long Short-Term Memory network. An LSTM adds a set of small internal “gates” that explicitly decide what to keep, what to forget, and what to pass forward at every step. It’s a more deliberate kind of memory than a plain RNN’s, and it’s why LSTMs could handle noticeably longer sequences before transformers came along.
RNNs and LSTMs were the backbone of machine translation, voice assistants, and predictive text for most of the 2010s. They’re still a reasonable choice today for time-series forecasting and other sequence problems that don’t need the scale of a modern language model — but for most language tasks, they’ve been overtaken by the next architecture on this list, which solves the same “remembering earlier context” problem in a fundamentally different way. LegacyVia’s natural language processing guide covers how these sequence models fit into the broader language-AI picture.
Transformers: Why RNNs Got Replaced
A transformer solves the same problem an RNN and LSTM were built for understanding a sequence — but it throws out the step-by-step approach entirely.
Instead of reading a sentence one word at a time and carrying a memory forward, a transformer uses a mechanism called self-attention to look at every word in a sentence against every other word, all at once, and learn which pairs matter to each other. In “the dog bit the man because it was scared,” self-attention is what lets the model figure out that “it” almost certainly refers to “the dog,” without having to process the sentence in strict left-to-right order.
Two things fall out of that design, and both turned out to matter enormously:
- It scales. Because a transformer doesn’t have to wait for step three before it can start step four, the calculations can run in parallel on a GPU instead of one after another. That parallelism is a large part of why it became possible to train models on a meaningful fraction of the internet in a practical amount of time.
- It keeps context better. Self-attention doesn’t have the “fading memory” problem an RNN has, since every word has a direct line to every other word rather than a chain of hand-offs.
The transformer architecture, introduced by Google researchers in 2017, is the foundation underneath ChatGPT, Claude, Gemini, and effectively every major large language model built since. LegacyVia’s deep learning guide goes further into the architecture and the “Attention Is All You Need” paper that started it, and the history of AI guide covers where the moment sits in the broader AI timeline.
Generative Adversarial Networks (GAN): Two Networks, Competing
Every architecture so far has one network doing one job classifying, predicting, translating. A GAN is different: it’s two neural networks trained against each other.
- The generator starts out producing more or less random noise and tries to turn it into something that looks like real data a face, a landscape, a signature.
- The discriminator is shown a mix of real examples and the generator’s fakes, and its only job is to guess which is which.
Early on, the generator is bad at its job and the discriminator catches it easily. But every round of training pushes both networks to improve the generator gets better at fooling the discriminator, and the discriminator gets better at spotting fakes until the generator’s output becomes difficult for even a trained human eye to identify as artificial. That back-and-forth competition is where the “adversarial” in the name comes from.
GANs were behind much of the first wave of AI-generated images and the technology underneath early deepfake videos, and they’re still used today anywhere a model needs to generate realistic output quickly real-time video game textures, face filters, and upscaling low-resolution images into sharper ones. For static AI art and image generation specifically, GANs have mostly been overtaken by a newer family called diffusion models, which build an image by gradually removing noise rather than through a generator-versus-discriminator contest the technique behind tools like Midjourney and Stable Diffusion. Industry researchers still project meaningful growth for GAN-specific applications, with one 2026 market report estimating the space will grow past $170 billion by 2035, largely on the back of fraud detection and synthetic data generation rather than art. LegacyVia’s generative AI guide covers how GANs, diffusion models, and language models all fit under the same generative AI umbrella.
Quick Comparison
| Type | Built For | Good At | Real-World Example |
|---|---|---|---|
| Feedforward (FNN) | Structured, tabular data | Basic prediction & classification | Credit scoring models |
| Convolutional (CNN) | Images, video, grid-shaped data | Spotting visual patterns | Face ID, medical imaging |
| Recurrent / LSTM | Sequences, time-based data | Remembering order and context | Time-series forecasting |
| Transformer | Sequences, especially language | Understanding context at scale | ChatGPT, Claude, Google Translate |
| GAN | Any data type it’s trained on | Generating new, realistic data | Deepfakes, data augmentation |
Which One Should You Learn First?
If you’re new to this and want the concepts to click quickly, start with CNNs. The idea of a filter sliding across an image and picking out edges is easy to picture, and it builds directly on the neuron-and-layer basics from LegacyVia’s neural networks guide. From there, RNNs make sense as “the sequence version of the same idea,” and transformers make sense as “the faster, better-at-memory upgrade to RNNs.” GANs are worth understanding last, since the two-networks-competing setup is a genuinely different mental model from everything else on this list.
You don’t need to be able to build any of these from scratch to use AI tools well. Knowing what each architecture is for is usually enough to understand why a given product behaves the way it does — and why, for example, an image generator and a chatbot are doing fundamentally different things under the hood, even though both are technically “neural networks.”
Why It Matters
None of these architectures are competing replacements for each other so much as different tools for different shapes of data. A modern AI product often stitches several of them together without you ever seeing the seams — a voice assistant might use one type of network to convert your speech to text and a transformer to figure out what you meant. Once the differences between CNNs, RNNs, transformers, and GANs make sense, most AI news stops sounding like buzzwords and starts sounding like specific, understandable engineering decisions.
For the fundamentals these architectures build on, LegacyVia’s guides on what machine learning is and what artificial intelligence actually is cover the broader field these all sit inside.
Frequently Asked Questions
What is the most common type of neural network? There isn’t a single “most common” type it depends on the data. Convolutional neural networks dominate image tasks, and transformers dominate language tasks, which together cover most consumer AI products people use daily.
Which type of neural network is best for image recognition? Convolutional neural networks (CNNs). Their filter-based design is specifically built to detect spatial patterns like edges, shapes, and textures in images.
Which type of neural network is used for text and language? Transformers are the standard choice today, powering tools like ChatGPT, Claude, and Google Translate. Recurrent neural networks (RNNs) and LSTMs handled this role before 2017 and are still used for some sequence tasks outside of language.
Are transformers a type of neural network? Yes. A transformer is a neural network architecture built around a mechanism called self-attention, which lets it process an entire sequence at once instead of step by step like an RNN.
What’s the difference between a GAN and a diffusion model? A GAN uses two competing networks a generator and a discriminator to produce realistic output. A diffusion model works differently, starting from random noise and gradually removing it in steps to reveal an image. Diffusion models have largely replaced GANs for tools like Midjourney and Stable Diffusion, though GANs are still used where speed matters more than perfect quality.
Do I need to know all these types to use AI tools effectively? No. Understanding what each architecture is generally good at is enough to make sense of most AI products. Building or training one of these yourself requires deeper technical knowledge, but using tools built on top of them doesn’t.
