What Is Deep Learning? A Plain-English Explanation

Deep learning is the quiet engine behind a lot of the AI you use without thinking about it: voice typing, photo search, translation and the chat assistants everyone's talking about. The term gets thrown around as if it were a different thing from AI altogether, so let me place it for you in plain English.

Deep learning explained in plain words

Wikipedia calls deep learning a branch of machine learning that uses multilayered neural networks for tasks like classification, regression and representation learning. Put simply, the computer learns patterns from lots of examples, and it does so through networks with many stacked layers.

Picture a series of filters. The first layer picks up simple things, like edges in a photo or single sounds in speech. Later layers combine those into bigger things, like shapes, then faces, or syllables, then words. Wikipedia describes this as a hierarchy of layers that turns input data into increasingly abstract representations.

Why it's called deep

The word refers to the number of layers, not to the depth of the thinking. More layers let the network build up richer ideas. There isn't an agreed line, either: Wikipedia states that no universally agreed-upon threshold of depth divides shallow learning from deep learning, so be wary of anyone quoting an exact number as if it were a rule.

Deep learning compared with older machine learning

The biggest change is who does the hard thinking about what matters in the data. Wikipedia explains that earlier machine learning often relied on hand-crafted feature engineering, while deep models learn useful features from the data on their own.

Question	Older machine learning	Deep learning
Who picks what to look for?	Usually a person	The network works it out
Data needed	Less	Generally far more (IBM)
Computing power	Modest	Generally far more, with GPUs helping
Good at	Tidy, structured data	Images, audio, language
Easy to explain?	Usually reasonably	Frequently called a black box

What deep learning powers in everyday life

Wikipedia says deep learning is part of state-of-the-art systems in computer vision and automatic speech recognition, and has been applied to areas like medical image analysis and drug design. IBM adds generative AI, self-driving cars and robotics. For a business owner, that shows up as:

• Dictation and meeting transcripts that finally get your accent right.
• Chat assistants that draft and summarise text.
• Image tools that generate or edit pictures from a description.
• Spam filters, product suggestions and automatic photo tagging.

If images are your interest, my guide to AI images for business shows how this technology gets used in practice.

How a deep learning model gets trained

Training is the learning phase. The model is shown a huge number of examples, makes guesses, measures its mistakes and adjusts its settings, over and over. IBM notes that deep learning generally needs far more training data and computing power than classic machine learning, and that graphics processors (GPUs) helped make it practical at scale.

Wikipedia mentions that early work on speech was held back by a lack of training data and limited computing power. That gives you a useful way to remember why this took off when it did: the idea is older than the hardware that could run it well.

The limits of deep learning you should know about

• Black box answers. IBM says the internal parameters rarely map to real-world meaning, so models are often called black boxes.
• Overfitting. Wikipedia warns that deep networks can capture rare quirks in the training data and then perform worse on new data.
• Heavy demands. The more layers, the more data and computing power are needed.
• Inherited bias. A model learns from its examples, so patchy examples can mean patchy answers.

This is why I always suggest checking an assistant's work rather than assuming it's right. My piece on where ChatGPT gets its information explains the data side in more detail.

Deep learning, machine learning and AI: the family tree

It helps to see the nesting. AI is the broad field. Machine learning is a way of building AI by learning from data. Deep learning is a type of machine learning using many-layered neural networks. And the large language models behind chat assistants are deep learning models trained on text.

You don't have to build any of this yourself. What matters is knowing what you're buying into when a tool says it's powered by deep learning: strong pattern spotting, plenty of data behind it, and answers that still need your judgement.

Using deep learning tools sensibly in your business

1. Pick one repeat task, such as transcribing calls or drafting first-pass replies.
2. Try a tool on a few real examples and check every result against what you know.
3. Read the tool's privacy and data controls page before uploading customer information.
4. Keep a person responsible for anything that goes to a customer.

If you'd like help choosing and setting up the right tools, take a look at my work as an AI consultant.

Frequently asked questions

What is deep learning in simple terms?

It's a way for computers to learn patterns from large amounts of data using neural networks with many layers. It powers things like speech recognition, photo tagging and chat assistants.

What is the difference between deep learning and machine learning?

Deep learning is a kind of machine learning. Older methods often needed people to hand-pick the features to look at, while deep models learn those features from the data themselves.

Is deep learning the same as AI?

No. AI is the wide field. Deep learning is one technique inside it, though it sits behind many of today's best-known AI tools.

Why is it called deep?

Because the neural network has many layers. Wikipedia notes there's no universally agreed threshold for how many layers make a network deep.

What are the downsides of deep learning?

It generally needs lots of data and computing power, can overfit its training data, and is often a black box, meaning it's hard to explain why it gave an answer.