Deep learning is a subset of machine learning that uses neural networks with many layers to learn complex patterns from data. It is the technology behind most of today’s advanced AI, from image recognition and speech recognition to the large language models behind ChatGPT. It changed artificial intelligence by letting systems learn useful features from raw data, instead of relying on people to define them.
What you’ll take from this
This guide is for professionals who use AI at work and want to understand what powers it. If you want to apply these tools in your job, Founderz’s Certificate Program in Artificial Intelligence and Innovation focuses on that practical side.
- Understand what it is and how it differs from ML.
- See how a layered neural network learns, in plain terms.
- Know the main deep learning algorithms and where each one is used.
- Recognize the limits and risks before you rely on one.
What is deep learning in AI?
Deep learning is a way of building AI systems with an artificial neural network that has several layers between input and output, a hidden layer stack of interconnected nodes. According to Wikipedia’s entry on deep learning, “deep” refers to the number of layers, which can range from three to hundreds or thousands.
Each layer transforms the data a little, passing its output to the next. Early layers detect simple features, such as edges in an image; later layers combine them into faces or objects. This automatic feature learning is the core of the method. That layered structure, loosely inspired by the neuron in the human brain, is what lets it handle images, audio and text.
Deep learning vs machine learning: does deep learning come under AI?
Yes. It sits inside machine learning, which sits inside artificial intelligence. Think of three nested circles: AI is the broad goal of machines performing tasks that need human intelligence, machine learning is the approach of learning from data, and deep learning is the branch that uses many-layered networks.
| Machine learning | Deep learning | |
|---|---|---|
| Features | Machine learning models often need manual feature engineering | A deep learning model learns features from raw data |
| Data needed | Works with smaller datasets | Needs large amounts of training data |
| Computational power | Runs on ordinary hardware | Usually needs GPUs or specialized chips |
| Explainability | Easier to explain, for example a decision tree | Harder to explain |
| Best for | Structured data in tables | Images, audio, language and big data |
For many business problems with tidy spreadsheet data, classic machine learning algorithms are cheaper and good enough. Neural nets win when the input is messy and the patterns are too intricate for people to describe. Many teams use machine learning and deep learning side by side.
How does a deep neural network learn?
A neural network learns by adjusting millions or billions of internal weights until its outputs match the examples it’s shown. The training process repeats a few steps many times.
- Input data, such as a labelled image, passes through the network layer by layer.
- Each node applies an activation function and passes the result on to the next layer.
- The network produces an output, for example “cat”, and a loss function measures how wrong it is.
- A mathematical optimization method adjusts the weights slightly to reduce that error.
- The cycle repeats over the whole dataset until accuracy stops improving.
This needs a lot of computing, which is why the field took off once graphics chips (GPUs) and large datasets became available. A turning point came in October 2012, when the AlexNet network, built by Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton, won the ImageNet competition by a wide margin over other methods.
What are the main deep learning techniques and algorithms?
They are built from a few network architectures, each suited to a different kind of data. These are the ones you’ll meet most often.
- Convolutional neural networks (CNNs): designed for images, used in computer vision, image classification and medical imaging.
- Recurrent neural networks (RNNs): designed for sequences such as audio or time series, now partly replaced by transformers.
- Transformers: introduced in the 2017 paper Attention Is All You Need, they power large language models and most text and image generators.
- Generative adversarial networks (GANs): two networks compete to create realistic images or data.
Two training approaches extend these architectures. Deep reinforcement learning combines neural networks with trial-and-error learning, as in game-playing systems. Self-supervised learning lets models learn from unlabeled data by predicting missing parts of it, which is how language models are pre-trained on huge amounts of text.
How is deep learning changing artificial intelligence?
It turned AI development from a set of narrow, hand-built rules into systems that learn directly from data at scale. Most tools people use daily, from voice assistants to translation and recommendations, run on deep learning systems.
The research community recognized the shift. Yoshua Bengio, Geoffrey Hinton and Yann LeCun received the 2018 Turing Award for breakthroughs that made deep neural networks a critical component of computing, and Hinton shared the 2024 Nobel Prize in Physics with John Hopfield, according to Wikipedia’s profile of Hinton.
Generative AI is the most visible result of the power of deep learning. Tools that write, summarize, create images or analyze sentiment in customer reviews all depend on it, and businesses using AI now rely on them every day. Our overview of generative AI tools shows what they can do at work.
Where is deep learning used? Use case examples from computer vision to speech
It is used wherever there is a lot of unstructured data and a pattern worth finding. These real-world AI applications depend on it.
- Computer vision: quality inspection in factories, medical image analysis and self-driving cars.
- Natural language processing (NLP): chatbots, translation, document summaries and sentiment analysis.
- Speech recognition: transcription, voice assistants and call analytics, all powered by deep learning.
- Science: drug design, protein research and climate modelling.
In companies, most people meet this technology through software rather than by building models. Eurostat reports that 19.95% of EU enterprises used at least one AI technology in 2025, with text mining the most common at 11.75%, according to its statistics on AI use in enterprises.
What are the limitations of deep learning?
It is powerful but has clear limits, and they matter for ethical AI. These are the main ones.
- Data hunger: it needs large, representative datasets, and biased data produces biased results.
- Cost: training large models takes significant computing power and energy.
- Opacity: it’s hard to explain why a model made a particular decision.
- Errors: models can make confident mistakes, including invented facts in chatbot answers.
The practical rule is simple: use it where its accuracy pays off, and keep a person checking decisions that affect customers, money or safety.
Further reading
These resources go deeper into how neural networks work.
- Deep learning (Wikipedia)
- Attention Is All You Need (arXiv, 2017)
- Convolutional neural network (Wikipedia)
Frequently asked questions
Short answers to common questions, starting with a deep learning definition.
What is deep learning in simple words?
It is a way of teaching computers by example. Instead of writing rules, you show a neural network many examples, such as photos labelled “cat” or “dog”, and it learns the patterns itself. It then applies those patterns to new examples it hasn’t seen.
Is ChatGPT deep learning?
Yes. ChatGPT runs on large language models built with the transformer architecture. They’re pre-trained on large amounts of text without labels, then refined with human feedback to follow instructions.
Can deep learning models make mistakes?
Yes, often with confidence. A model can misclassify an image it hasn’t seen before, reflect bias in the data it learned from, or produce text that sounds right but is false. That’s why important outputs need human review.
Do I need to code to use deep learning?
Not to use it. Most professionals use it through tools such as ChatGPT, Copilot or image generators. Building and training your own models usually needs programming, often in Python, plus data science skills.
You don’t need to train neural networks to get value from AI and machine learning; you need to know where it helps and where it fails. The Certificate Program in Artificial Intelligence and Innovation teaches professionals to apply AI tools to their own work, and you can see the full syllabus on the program page.
