Mixing up machine learning vs deep learning is easy, and you’re not alone. Deep learning is a subset of machine learning that uses layered neural networks to learn features automatically, where classic machine learning often relies on features people design.
For the bigger picture, read the explainer on what is artificial intelligence, which places both approaches inside the wider field.
| Item | Value |
|---|---|
| Machine learning data need | Small to medium datasets |
| Deep learning data need | Very large datasets |
| Compute needs | Moderate for machine learning, high for deep learning |
| Best suited to (machine learning) | Structured data |
| Best suited to (deep learning) | Unstructured data |
These figures come from the Databricks comparison of machine learning and deep learning. They are general guidance and can change, so check the source before you rely on them.
How machine learning and deep learning fit inside AI
Artificial intelligence is the broad field of building intelligent systems that perform tasks requiring human-like decision making. Machine learning and deep learning are nested approaches inside it, not rivals of AI itself.
Databricks describes machine learning as a type of AI where systems learn patterns from historical data, without being explicitly programmed for every rule. Deep learning is a specialized subset of machine learning that uses multi-layer neural networks to learn from large datasets.
The same source lists the typical techniques at each level:
- AI: rules, logic and search.
- Machine learning: statistical models.
- Deep learning: neural networks.
What does machine learning do?
Machine learning lets a computer learn patterns from data and then use them to make predictions or decisions. A Dataquest tutorial shows this with football results: a model can learn from tabular match data and make future predictions based on what it learned.
Many different models sit under this label. Databricks names several common ones:
- Decision tree: learns a set of if-then rules from data.
- Random forest: combines a collection of decision trees to make more accurate and stable predictions.
- Support vector machines: find the best boundary that separates data points into different classes.
- Linear regression: models the relationship between what you want to predict and your inputs.
An IT Pro feature notes that supervised, semi-supervised, unsupervised and reinforcement learning are all strands of machine learning. In plain terms:
- Supervised learning trains on labeled data, where inputs are paired with output labels, as Salesforce explains.
- Unsupervised learning works with unlabeled data to discover patterns, structures or relationships.
- Reinforcement learning learns from interaction and rewards, for example training a robotic arm to pick and place objects.
Machine learning also leans on feature engineering. Dataquest describes it as modifying the features of your data, and calls it a common but important part of any machine learning workflow that can improve a model’s performance.
What deep learning adds
Artificial neural networks sit at the core of deep learning. Data enters, passes through hidden layers of nodes that extract features, and an output layer predicts a value.
The “deep” in the name refers to how many hidden layers a model has. The key change is that features are learned rather than hand-built: one explainer says deep learning is distinguished by deep networks with many layers that learn complex and abstract relationships without explicit feature engineering.
IT Pro lists several deep learning architectures:
- Convolutional neural networks are mostly used for image processing and object detection.
- Recurrent neural networks use sequential or time series data, such as in language translation and speech recognition.
- Generative adversarial networks produce new data instances that look like the training data.
If you want to see what happens inside those layers, the guide on how do neural networks work walks through it step by step.
Data, compute and feature work: the practical differences
The biggest everyday difference is how much the approach demands from you. Databricks summarizes it this way: deep learning typically needs more data and compute, but can capture more complex patterns in images, audio and text.
There is a technical reason for the data appetite. According to IoT For All, deep learning architectures have many more parameters and so need more data to avoid overfitting.
In practice, the trade-offs look like this:
- Data: machine learning copes with small to medium datasets; deep learning wants very large ones.
- Compute: moderate for machine learning, high for deep learning.
- Human effort: machine learning often depends on crafted features; deep learning extracts them itself.
More data is not automatically better data. Dataquest points out that it is not just about the quantity of the data but also its quality, and you can see how weak data shows up in results in these ai bias examples.
Which one should you choose for your problem?
Databricks says teams select methods based on problem complexity, data and resource constraints. A simple way to apply that is to work through these steps:
- Look at your data type. Structured data suits machine learning; unstructured data such as images, audio and text suits deep learning.
- Check how much data you have. Small to medium datasets point to machine learning.
- Check your compute budget. Deep learning needs high compute.
- Judge the complexity of the task. Dataquest says deep learning models can handle complex tasks that machine learning models are often ill-suited to, such as transcribing and summarizing podcasts.
Databricks lists recommendations as a typical machine learning example and vision, speech and language as typical deep learning examples. Both approaches are used in practice, so the better question is which one fits your data and resources.
Frequently asked questions
Is deep learning the same as machine learning?
Show answer
No. Deep learning is a specialized subset of machine learning that uses multi-layer neural networks, as the Databricks comparison describes it.
Does deep learning need more data?
Show answer
Typically yes. Databricks says deep learning typically needs more data and compute, and IoT For All explains that its many parameters call for more data to avoid overfitting.
Is deep learning always the better choice?
Show answer
Not necessarily. Databricks says teams choose based on problem complexity, data and resource constraints, and lists structured data as the best fit for machine learning.
Are neural networks the same as deep learning?
Show answer
Not quite. IoT For All describes neural networks as a type of machine learning algorithm, and deep learning as the subfield that focuses on neural networks with multiple layers.
