Deep learning is machine learning that uses neural networks with many layers. Each layer transforms the data a little. Stacked together, the layers learn representations that a simple model would need you to design by hand.
What a neural network is
A neuron takes several inputs, multiplies each by a weight, adds them up, and passes the result through a function that decides how strongly to fire. A layer is many neurons working in parallel. Training adjusts the weights so the network’s output gets closer to the right answer. That adjustment is driven by a loss (how wrong the output is) and an optimizer such as gradient descent.
| Idea | Plain meaning |
|---|---|
| Layer | One transformation of the data |
| Weight | A number the model learns |
| Loss | How far the prediction is from the truth |
| Epoch | One full pass through the training data |
| Deep | Many layers, so the model can learn complex patterns |
Why depth matters
Early layers pick up simple signals: edges in an image, or local word patterns in text. Later layers combine those signals into objects, topics, or intent. That is why the same family of models can read a scan, transcribe speech, or power a language model.
Where you meet it
- Image tasks, such as reading a document or spotting a defect.
- Speech recognition and translation.
- Large language models, which are deep transformer networks.
A practical example
A claims team receives photos of vehicle damage. A deep network trained on labeled photos estimates the damaged panels and a rough severity. An adjuster confirms the estimate. The network replaced a long list of hand-built visual rules.
What to remember
- Deep learning is ML with multi-layer neural networks.
- It shines when the raw input is text, images, or audio.
- It needs more data and compute than a linear model, and it is harder to explain line by line.
