Deep Learning vs Machine Learning: What's the Difference?

One idea, two labels

Is deep learning something different from machine learning? The two terms are sometimes used as synonyms, and it helps to keep them apart. The short answer: deep learning is a specialised subfield of machine learning, and “deep” refers to the many layers of a neural network (Syracuse University School of Information Studies, 22 September 2025).

Put another way, all deep learning is machine learning, but not the other way round. Let’s look at what changes from one to the other, when each approach tends to suit, and what limits it has. If you need a starting point, we have a guide to what machine learning is.

Classic machine learning: you decide what to look at

Machine learning starts from a simple idea: instead of programming rules by hand, you give a model data and let it learn patterns. There are many families of algorithms; you can go through them in the most common types of machine learning algorithms.

In many traditional approaches, people decide in advance which information the model should pay attention to. This task is called feature engineering (Syracuse). An example: to classify images of cats and dogs, you have to configure features by hand, such as the shape of the eyes, the tail, the ears or the outline of the nose (AWS, comparison documentation from a cloud services provider).

What makes deep learning “deep”

LeCun, Bengio and Hinton define it as computational models composed of multiple processing layers that learn representations of data with multiple levels of abstraction (Nature, 2015). In plain words: a deep neural network has three or more layers of nodes, counting the input and output layers (AWS). If you want to see the component from the inside, how neural networks work explains it step by step.

Here is the counterpoint to the previous section. Deep learning can analyse raw data and discover for itself what matters most. With an image, the first layers may detect edges, the middle ones shapes or textures, and the final ones objects such as faces or cars (Syracuse).

How does it learn? Information flows forward, from input to output. The difference between the predicted output and the actual one is calculated, and that error is propagated backwards (backpropagation) to adjust the weights of the neurons (AWS). The Nature article describes it as the way of indicating how the machine should change the internal parameters it uses to compute the representation in each layer from the one before. A parameter, to put it simply, is one of the model’s adjustable internal values.

Differences that matter in practice

Who chooses what to look at: in classic machine learning it is usually the person; in deep learning, the model discovers the features from the raw data (Syracuse).
Amount of data: classic machine learning tends to perform better with little data (hundreds or thousands of examples); deep learning tends to require large datasets, of thousands or millions (Syracuse).
Type of data: deep learning is better suited to unstructured data, such as images or natural language (AWS).

Why does the data matter? With few examples, a model with many parameters risks memorising the training examples instead of learning general patterns (Syracuse). And note the nuance: the sources speak of tendencies (“tends to”), not fixed rules.

Why it took off in 2012 and not before

Neural networks were not a new idea. For decades, Geoffrey Hinton, Yann LeCun and Yoshua Bengio stuck with them while a good part of the AI community saw them as a dead end and preferred symbolic approaches, that is, rules of logic coded by hand rather than learned (MIT Technology Review, 27 March 2019).

In 2012, deep networks suddenly proved to be extraordinarily good at image recognition. The key was feeding them enormous amounts of training data and running them on powerful graphics chips (graphics processing units, or GPUs), which are well suited to parallel computation (MIT Technology Review). The three researchers later received the Turing Award, worth 1 million dollars. The lesson: there was no sudden trick, but years of work, more data and more hardware.

Where it has got to: image, speech, text

According to Nature (2015), these methods have markedly improved the state of the art in speech recognition, visual object recognition and object detection, and also in areas such as drug discovery and genomics. Broadly speaking, deep convolutional networks have brought breakthroughs in images, video, speech and audio, and recurrent networks have shed light on sequential data such as text and speech.

Within deep learning itself, architectures also change. In 2017, Vaswani and colleagues proposed the Transformer, based solely on attention mechanisms and dispensing with recurrence and convolutions (arXiv, 12 June 2017). The authors present it as, to the best of their knowledge, the first transduction model relying entirely on self-attention.

And the numbers? Here it is worth separating two things. The reference test (benchmark) is the WMT 2014 English-to-German translation task; the metric is BLEU, the score used to measure translation quality on that task. On it the Transformer reached 28.4 BLEU, more than 2 points above the best previous results, including ensembles of models. The large model was trained for 3.5 days on 8 P100 GPUs. The sources consulted give no further detail on how WMT is organised. And remember that this is a single task: the figure says nothing about others.

The limits, and what we still don’t know

Data and compute: with little data, the risk of memorising is real (Syracuse), and the success of 2012 depended on massive data and graphics chips (MIT Technology Review).
Risks noted: deep learning has boosted facial recognition and other forms of surveillance, and has concentrated power in companies with lots of data and computing capacity (MIT Technology Review).
What the sources consulted do not detail: how to interpret what happens inside the layers, or how much these training runs cost in general.

And a warning about what the result doesn’t say: improving on a benchmark is not the same as being better at every task.

My take, and where to go next

This is my opinion. I think the useful distinction is not “old versus new”, but who decides which representation to use, the person or the model, and how much data there is to let the model decide. Depending on the problem, one option or the other will make more sense. And the story of the three prizewinners reminds me that almost every advance that arrives looking like a leap has decades of work behind it.

If you want to see how a network works on the inside, move on to Mar’s article on how neural networks work.

Sources

Image: geralt on Pixabay

Translated and adapted by Ollie.

Deep Learning vs Machine Learning: What’s the Difference?