Neural Network vs. Deep Learning: What's the Difference?
These overlap heavily — one is the tool, the other is how you use it. A neural network is a model loosely inspired by the brain: layers of connected 'neurons' that learn patterns from data. Deep learning is the practice of using neural networks with many layers (hence 'deep') to tackle complex problems like image and speech recognition. So every deep-learning model is a neural network, but a simple one- or two-layer neural network isn't usually called deep learning.
See the difference, explained visually.
Watch a 2-minute animated lesson comparing neural network and deep learning.
At a glance
| Neural Network | Deep Learning | |
|---|---|---|
| What it is | A brain-inspired model structure | Using many-layered neural networks |
| Depth | Can be shallow (1–2 layers) | Many hidden layers ('deep') |
| Relationship | The building block | Neural networks applied at depth |
| Best for | Simple pattern problems | Complex data: images, speech, text |
| Needs | Less data and compute | Lots of data and compute |
Which should you use?
Neural Network
Say 'neural network' when you mean the model structure itself — the layers of neurons, shallow or deep.
Deep Learning
Say 'deep learning' when you mean the approach of stacking many layers to learn complex patterns automatically.
Frequently asked questions
- Is a neural network the same as deep learning?
- Not exactly. A neural network is the model; deep learning means using neural networks with many layers. All deep learning uses neural networks, but a shallow neural network isn't deep learning.
- What makes learning 'deep'?
- The number of layers. 'Deep' refers to having many hidden layers between input and output, which lets the network learn increasingly abstract features.
- How do they relate to machine learning?
- Both sit inside machine learning. Neural networks are one family of ML models; deep learning is the branch that uses deep neural networks.