US How machine learning and deep learning relate
Machine learning is the branch of artificial intelligence in which a computer learns patterns from data instead of following hand-written rules. Deep learning is one family of machine learning methods, built on artificial neural networks with many layers. Every deep learning model is a machine learning model, but most machine learning in daily use is not deep learning.
The terms nest inside each other. Artificial intelligence is the broad goal of machines doing tasks that need human-like judgment. Machine learning is the main way to get there today. Deep learning is the subset behind modern computer vision, speech recognition and large language models, and generative AI sits inside deep learning.
When people say “machine learning vs deep learning”, they usually mean classic machine learning, the statistical algorithms that came before deep networks, compared with deep neural networks.
How classic machine learning works
Classic machine learning starts with features: measurable properties of the data chosen by an engineer or data scientist. For a motor, features might be vibration RMS, peak frequency and temperature. The algorithm learns how those features relate to an outcome, such as healthy or about to fail. Common algorithm families include:
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Linear and logistic regression. Fit a straight-line or S-shaped relationship to predict a value or a yes/no class. Fast and easy to explain.
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Decision trees, random forests and gradient boosting. Split the data by a series of feature thresholds. Gradient-boosted trees, such as XGBoost, are often the most accurate choice for tabular data.
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Support vector machines. Find the boundary that best separates classes, which works well on small, clean datasets.
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Clustering such as k-means. Groups similar records without labels, used for segmentation and spotting unusual behavior.
How deep learning differs
A deep learning model skips the hand-chosen features. Raw pixels, audio samples or text tokens go straight into a neural network, and its layers learn which features matter: early layers in an image model pick out edges and textures, later layers recognize parts and whole objects. Training adjusts millions or billions of weights by backpropagation until the output matches labeled examples.
That is why deep learning dominates images, video, speech and language, where good features are almost impossible to design by hand. The cost is data and compute. A deep network typically needs thousands to millions of examples, and its training runs as large matrix calculations that suit a GPU far better than a CPU.
Machine learning vs deep learning compared
| Aspect | Classic machine learning | Deep learning |
|---|---|---|
| Features | Designed by people from domain knowledge | Learned automatically from raw data |
| Data needed | Hundreds to thousands of examples can work | Usually thousands to millions of examples |
| Best data types | Tables, logs, sensor readings | Images, video, audio, text |
| Training hardware | CPU is usually enough | GPU or AI accelerator |
| Inference hardware | Any industrial PC or microcontroller | GPU, Jetson module or NPU for real-time use |
| Explainability | Often easy to see why a decision was made | Harder; needs extra tools to interpret |
| Edge example | Predicting bearing wear from vibration data | Finding defects in camera images on a line |
Neither is better in general. On structured, tabular data, gradient-boosted trees often match or beat neural networks with less effort. On unstructured data, deep learning is usually far ahead.
Which to use for an industrial or edge application
The data type usually decides. A few practical rules:
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Sensor and process data. Vibration, current, temperature and pressure readings for predictive maintenance are often handled well by classic machine learning, running on the CPU of an existing industrial PC.
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Images and video. Inspection, people counting, ALPR and robot guidance use deep learning, the core of modern machine vision.
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Speech and text. Voice commands, transcription and document search use deep learning models, increasingly small language models running on site.
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Little data, or decisions that must be explained. Start with classic machine learning. It trains on small datasets and its reasoning is easier to show an auditor or operator.
Hardware for each approach
Classic machine learning rarely drives the hardware choice: a fanless industrial PC runs it alongside other software. Deep learning inference, especially on several camera streams, needs an accelerator. Our edge AI computers cover NVIDIA Jetson modules, GPU cards and Hailo or Intel NPUs, so a vision model and a sensor model can share one system. For definitions of the wider vocabulary, see our glossary of AI and GPU computing terms.
Quick answers on machine learning vs deep learning
Machine learning and deep learning: common questions
Is deep learning machine learning?
Yes. Deep learning is a subset of machine learning that uses multi-layer neural networks. All deep learning is machine learning, but not all machine learning is deep learning.
What is the difference between AI, machine learning and deep learning?
They nest inside each other. AI is the broad field, machine learning is the approach of learning from data, and deep learning is the neural-network branch of machine learning.
Is deep learning supervised or unsupervised?
It can be either. Most industrial vision models are trained supervised, on labeled images. Large language models are pretrained self-supervised, using the text itself as the label, and autoencoders learn without labels.
When should you use deep learning instead of machine learning?
When the data is unstructured and plentiful. Images, audio and text with thousands of examples favor deep learning. Small tabular datasets usually favor classic machine learning.
Do you need a GPU for machine learning?
Not usually. Classic machine learning trains and runs on a CPU. A GPU or other AI accelerator becomes necessary for deep learning, particularly for training and real-time video inference.
We supply industrial PCs and edge AI computers with the operating system, drivers and AI runtime installed and validated as standard. Tell us your data sources and models, and we will recommend whether a CPU, a Jetson module or a GPU fits the workload.