What is the difference between machine learning and deep learning?

What is the difference between machine learning and deep learning?

Machine learning is a family of methods that learn patterns from data. Deep learning is a subset of machine learning that uses neural networks with multiple layers of learned transformations.

They are related categories, so comparing them is similar to comparing vehicles with cars: one category includes the other.

How features are represented

A traditional machine-learning workflow often uses features selected or designed by people. To predict delivery duration, those features might include distance, package weight, departure time, and the number of stops.

Deep-learning models can learn useful internal representations from inputs such as images, audio, and text. In an image model, successive layers may combine local visual patterns into representations useful for recognizing objects. The exact meaning of each internal feature is not always easy to interpret.

Differences in practical tradeoffs

Deep learning can be powerful when inputs are complex and enough suitable data and computation are available. It can also be expensive to train and difficult to inspect.

Methods such as linear models, decision trees, and gradient-boosted trees remain useful, particularly for many structured-data problems. They can be strong baselines and may be easier to deploy or explain.

Choose by evidence

Suppose a shop wants to predict weekly demand using a modest table of sales records. A simple statistical or machine-learning model may be enough. If the task is describing product images, a pretrained deep-learning model may be more appropriate.

Evaluate candidates using the same held-out examples and practical constraints. A model's complexity is not a quality score. The right choice is the one that meets the task's accuracy, speed, cost, and maintenance requirements.