What is Deep Learning and how does it work?

Behind the magic of ChatGPT, DALL·E, and self-driving cars—meet Deep Learning.

Last time, we talked about Machine Learning, how AI recognizes patterns, makes predictions, and improves with experience.

Deep Learning takes it further. Instead of just spotting patterns, Deep Learning mimics how our brains process information using neural networks. This allows AI to handle more complex and abstract tasks like understanding language, recognizing faces, and even generating art.

That’s why some of today’s most advanced AI runs on Deep Learning:

• DALL·E → Creates images from just text prompts
• Tesla’s autopilot → Helps self-driving cars “see” and react in real time
• Facial recognition → Identifies people with scary high accuracy, in the dark with half my face showing and you know its me??

So while Machine Learning helps AI recognize patterns, Deep Learning enables AI to analyze, create, and make decisions in a way that feels more like us.

But how does it actually work? What makes it different from regular Machine Learning?

Read more: What is Machine Learning and how does AI actually learn?

How does Deep Learning work?

Think about when you’re trying to learn a new skill. You don’t just memorize facts, you practice, make mistakes, and improve over time.

Deep Learning works the same way. A chef doesn’t become an expert by just reading recipes. They refine their skills over time, by, adjusting based on taste, texture, and feedback.

Deep Learning does this, too, it constantly refines itself through experience. Here’s how it works:

1) AI is given data

Deep Learning needs massive amounts of raw data to get smart, the more, the better. For example, if you want AI to recognize dogs? Show it millions of dog images with different breeds, colors, and from different angles.

2) The neural network breaks it down

Instead of analyzing the whole image at once, AI processes it in layers. For example, when analyzing a photo of a dog it’ll first focus on the edges, then textures, and shapes, and finally it’ll combine everything together to decide: “Yea, that’s a dog.”

3) It learns & adjusts

AI isn’t perfect on the first try. It compares its guess to the correct answer and self-corrects by tweaking its internal settings. For example, If AI mistakes a cat for a dog, it updates and improves, similar to how we learn from experience.

But how does AI know what to tweak?

How does Deep Learning adjust itself?

That’s where Backpropagation comes in.

When AI makes a mistake, it doesn’t just charge it to the game, it adjusts its internal settings on its own, called weights, to get better over time.

Think of weights like recipe adjustments. If a cake turns out too dry, you tweak the amount of liquid needed next time.

AI does the same thing, if it mislabels a cat for a dog, backpropagation helps it figure out what part of its decision making was wrong by comparing its guess to the correct answer, calculating the error, and working backwards through the network to fix mistakes. So over time, it’ll learn that “cats tend to have pointier ears” and adjusts accordingly.

This cycle repeats, millions of times, making Deep Learning incredibly powerful.

Why is it called “Deep” learning?

The “Deep” in Deep Learning comes from how many layers AI uses to process information. The more layers, the more capable the model becomes. Each layer extracts more details:

🔹 Early layers → Detect basic shapes and textures.

🔹 Deeper layers → Recognize faces, emotions, speech, and context.

That’s why Deep Learning can understand sarcasm, or even predict what text should come next in a conversation. It can generate, adapt, and refine in a way that feels more like us.

For example, AI speech tools like ElevenLabs and 15.ai don’t just mimic our words, they study our tone, emotion, and even natural pauses to ensure the delivery sounds as natural as possible.

But not all AI needs Deep Learning.

An example of Shallow Learning is spam filters, where AI only needs 1-2 layers to recognize simple patterns. It doesn’t need 10+ layers of neurons to figure out “This email looks sus.“

When AI needs to process more complex tasks, like understanding language, recognizing images, or generating content, it needs something more powerful.

The different Deep Learning models

Deep Learning isn’t a one-size-fits-all kind of thing. Just like you wouldn’t use the same tool to edit a photo and write an essay, AI uses different model architectures depending on the task.

Some models are designed to see, some to remember, and others to understand:

1️⃣ Convolutional Neural Networks (CNNs) – AI That “Sees“

• CNNs are the vision experts of AI. They’re designed to recognize images, detect objects, and even classify videos. For example, Google Photos uses CNNs to sort pictures by face, even as people age or change hairstyles they’re still categorized properly.

2️⃣ Recurrent Neural Networks (RNNs) – AI That “Remembers“

• RNNs specialize in understanding sequences, whether that’s text, speech, or time based data. They’re used when AI needs to remember what came before to make better predictions. For example, Google Translate relies on RNNs to interpret full sentences, to ensure the full context is translated and not just each word is translated.

3️⃣ Transformers – AI That “Understands“

• Transformers are the most advanced deep learning models, designed for language understanding, content generation, and high-level reasoning. This model is used for language models and generative AI. For example, DALL·E uses Transformers to turn text descriptions into realistic images.

Transformers aren’t just for generating images from text, they’re the brains behind AI that understands.

Ever wondered how ChatGPT, Bard, or Claude can hold conversations, answer questions, and even generate entire essays?

That’s where Large Language Models (LLMs) come in.

🤖 Ctrl + AI + Del — Resetting the way we think about AI

Missed the first lessons? We covered “What is AI?” and then “What is Machine Learning?“

More from We Love Open Source

• What is Artificial Intelligence?
• The AI crash course I needed when I was new
• What is Machine Learning and how does AI actually learn?
• The best programming languages to learn first
• Why AI won’t replace developers

This article is adapted from “What is Deep Learning?” by Ebony Louis, and is republished with permission from the author.