What is deep learning? Explained for B2B marketers

Deep learning is a specialised form of machine learning that works with neural networks made up of many layers. Because of that depth the model can learn very complex patterns, in language, images and sound, without a human having to name those characteristics in advance. It is the technology behind just about every major AI breakthrough of recent years, from image recognition to the language models in AI search engines. In this article you will read what deep learning is, how it differs from ordinary machine learning and why it concerns you as a B2B marketer.

What is deep learning exactly?

Deep learning is a subfield of machine learning in which models learn through a layered neural network. The word “deep” refers to the number of layers: where a simple model has one or two layers, a deep learning model counts dozens to hundreds. Each layer builds on what the previous one has recognised.

Take image recognition. The first layers pick up simple things such as edges and colours. Later layers combine those into shapes, then into parts, and finally into a complete understanding such as “this is a face”. According to IBM that is the distinguishing feature: the model builds up increasingly abstract characteristics itself, instead of a human supplying them. That makes deep learning powerful for tasks that are too messy for fixed rules.

What is the difference with ordinary machine learning?

Deep learning is machine learning, but not all machine learning is deep learning. The difference lies in two things:

• Feature engineering. With classic machine learning a human often determines which characteristics are important. With deep learning the model discovers those itself from raw data.
• Scale. Deep learning only really thrives with lots of data and considerable computing power. With little data a simpler model often performs just as well or better.

In short: deep learning can handle more complex problems, but also demands more. For structured tabular data of limited size, a simpler model is often the smarter choice.

This table places the most important differences side by side:

Aspect	Classic machine learning	Deep learning
Determining characteristics	Often manually by a human	Automatically from raw data
Amount of data	Works with small datasets	Demands a lot of data
Computing power	Limited, runs on ordinary hardware	Heavy, often with GPUs
Type of problems	Structured data, clear rules	Messy data such as language, images and sound
Explainability	Reasonably easy to follow	Often a black box

Neither is “better”. It depends on your problem, your data and what you want to explain.

Why deep learning concerns you as a marketer

You do not have to build a model to come into contact with deep learning. It is already in the tools you use daily, and increasingly in the way your audience searches. A few examples:

• AI search engines and chatbots. The language models behind ChatGPT, Google Gemini and Perplexity are deep learning. They help determine which sources they cite when someone asks a question.
• Image and speech recognition in advertising tools and analytics software.
• Recommendation systems that estimate which content or products are relevant.

Deep learning has, incidentally, been in ordinary search results much longer than many people think. Since 2015 Google has used a system called RankBrain to better understand unusual searches, and since 2019 the language model BERT to grasp the intent behind a sentence instead of looking at keywords in isolation. Google describes in its overview of ranking systems how these models help understand the meaning of a search, even when you word it differently from the page you are looking for. For you that means: write the way your customer really talks, do not stack keywords.

That first category, the AI search engines, changes the game for B2B even more strongly. If your potential customer asks an AI for advice instead of scanning ten blue links, then you want the model to mention you. That is the core of generative engine optimization. Because these models learn from text and patterns, they reward clear, reliable and well-structured content. That is exactly what our GEO agency approach builds on further, as an extension of classic SEO.

Honestly: do you need to do something with deep learning yourself?

We are honest about it: most B2B companies do not need to train a deep learning model themselves. That demands a lot of data, expensive infrastructure and specialised people, and it rarely delivers more than an off-the-shelf tool that is already trained on billions of examples.

Where it does count is understanding how these models work so that you make smarter choices. Grasping that a language model predicts patterns and does not really “understand” explains why it sometimes confidently generates nonsense and why reliable source citation becomes so important. And as always: steer on customers and revenue, not on the newest technology because it sounds impressive. As a small team that moves fast, we choose what works, not what impresses.

How does deep learning fit into the bigger AI picture?

See it as a series of shells. Artificial intelligence is the broadest layer. Machine learning sits inside it, as the branch that learns from data. Deep learning is a specialisation within machine learning, based on deep neural networks. And the large language models, such as a large language model, are an application of deep learning to text. That is how it all hangs together.

Frequently asked questions

Is deep learning the same as machine learning? Deep learning is a part of machine learning that works with neural networks with many layers. All deep learning is machine learning, but a lot of machine learning uses no deep networks.

Why is it called “deep”? It refers to the number of layers in the neural network. More layers means the model can build up increasingly abstract patterns, from simple edges to complex meaning.

Does deep learning need a lot of data? Yes. Deep learning only performs well with large amounts of data and considerable computing power. With little data a simpler model often works just as well or better.

What does deep learning have to do with AI search engines? The language models behind tools like ChatGPT and Gemini are deep learning. They learn from enormous amounts of text and thereby determine which sources they cite, which makes it relevant for your visibility.

Ready to deploy AI smartly?

Tell us where you stand with AI and visibility, and we will tell you honestly what is and is not worth it. No technical show, but choices that deliver customers. Plan your free intake.