The relationship: nested, not separate
The first thing to fix is the framing — deep learning is not an alternative to machine learning, it is a subset of it. Machine learning is any system that learns patterns from data. Deep learning is the specific corner of that field that uses neural networks with many layers (“deep” refers to the number of layers). So the real comparison is between classical machine learning — decision trees, support vector machines, linear and logistic regression, gradient-boosted trees — and deep learning. Both learn from data; they differ in how they represent the problem and what they demand from you.
Feature engineering vs learned representations
The single biggest practical difference is who decides what the model looks at. Classical ML relies on feature engineering: a human chooses and shapes the input variables — “price per square metre,” “days since last purchase,” “ratio of capital letters” — and the algorithm learns from those crafted features. The model is only as good as the features you give it. Deep learning does representation learning: fed raw input like pixels or characters, its layers learn their own intermediate features automatically — edges, then shapes, then objects in vision; characters, then words, then meaning in language. This is why deep learning conquered image and speech tasks where useful features are nearly impossible to specify by hand.
Data, compute, and interpretability
These approaches trade off along three axes. Data: classical models work well with hundreds or thousands of examples; deep networks typically need tens of thousands to millions before their advantage shows. Compute: classical models train in seconds to minutes on a laptop; deep models often need GPUs and far longer. Interpretability: a decision tree or linear model can be read and explained — you can point to the rule that drove a decision — whereas a deep network is largely a black box, a serious drawback in regulated domains like lending or healthcare. None of these is “better”; they are constraints that should drive your choice.
When to use which
A useful rule of thumb: match the tool to the data. For structured, tabular data — spreadsheets of customers, transactions, sensor readings — start with classical ML, especially gradient-boosted trees, which routinely outperform neural networks on this kind of data while being cheaper and explainable. For unstructured, high-dimensional data — images, audio, video, and natural language — deep learning is usually the right and often the only viable choice. When you have little data or need to justify every decision, lean classical. When you have abundant data and raw perceptual inputs, lean deep.
Why this distinction still matters in the LLM era
Large language models are deep learning at its most extreme — billions of parameters, transformer layers, trained on internet-scale text. Their success can make it seem like deep learning is always the answer. It is not. Plenty of real-world problems are small, tabular, and explainability-critical, and there the old methods remain the professional’s choice. Knowing where classical ML ends and deep learning begins keeps you from reaching for a billion-parameter model when a decision tree would be faster, cheaper, and clearer.