Software sales pitches and tech news are full of terms like “powered by machine learning,” “deep learning” and “neural network,” and they are sometimes used loosely as if they meant the same thing. If you are a manager or IT professional, knowing the difference helps you understand what data, what hardware and what level of oversight a project needs.
How the concepts relate: nested circles
The simplest picture is three nested circles:
- Artificial intelligence is the largest circle: any method by which a computer does something “intelligent,” even with hand-written “if this, then that” rules.
- Machine learning is a subset of AI: instead of rules being written, the system learns patterns from data.
- Deep learning is a subset of machine learning: it uses neural networks with many layers.
A neural network is in fact the “structure,” or architecture, on which deep learning is built.
Machine learning
In conventional programming, the programmer writes the rules. In machine learning, you give the system a large number of examples and it finds the relationships itself. For instance, instead of writing “an email containing this word is spam,” you give the model thousands of labeled emails so it can learn the signs of spam on its own. There are three main approaches:
- Supervised learning: the data is labeled, such as emails marked “spam/legitimate” or invoices marked “normal/suspicious.”
- Unsupervised learning: the data has no labels, and the model finds groups or anomalies on its own, such as segmenting customers or detecting unusual network traffic.
- Reinforcement learning: the model learns by trial and error, receiving rewards or penalties; it is used mostly in robotics, games and optimization.
Classic machine learning methods, such as decision trees and regression, often work well with tabular data (such as Excel files or a sales database), do not need heavy hardware, and produce results that are easier to explain.
What is a neural network?
An artificial neural network is made of simple units called “neurons,” arranged in several layers. Each neuron combines its inputs using weights and passes the result to the next layer. During training, these weights are adjusted over and over to reduce the output error. The name “neural” comes from being inspired by the brain, but these networks are not a real simulation of the human brain.
Deep learning
When a neural network has many layers, it is called “deep.” The big advantage of deep learning is that it can extract important features by itself from raw, unstructured data such as images, audio and text. Speech recognition, machine translation, image recognition and today’s chatbots are all based on deep learning.
But this power comes at a cost:
- It needs very large amounts of data for training.
- Training usually requires graphics processors (GPUs) and high power and cooling consumption.
- It offers less explainability; it is often hard to say why the model reached a particular result.
For most of a company’s needs, you do not have to train a model from scratch. Using ready-made models, or fine-tuning an existing model on your own data, is usually far less expensive.
Which is right for which task?
- Tabular data of moderate size (sales forecasting, credit risk, inventory levels): classic machine learning is usually sufficient and easier to understand.
- Images, audio and free text (document recognition, call analysis, answering questions): deep learning has the edge.
- Clear, fixed rules: sometimes a simple script or rule is better, cheaper and more reliable than any model.
Checklist
- Before choosing a method, identify the type of data (tabular, image, text, audio) and its volume.
- Assess data quality and labeling; a good model trained on bad data will not give good results.
- If explaining decisions matters, give priority to simpler methods.
- For training deep models, estimate GPU, storage, power and cooling needs in advance.
- Back up training data like any other sensitive data, and restrict access to it.
If your machine learning project needs a powerful server or virtual machine, storage and data backup, ITPlus724 engineers can prepare that infrastructure for you on-site or in a data center.