Convolutional Neural Network (Classification)
Also known as: CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier
A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.
Key highlights
- Learns local patterns automatically, greatly reducing the need for manual feature engineering.
- Weight sharing recognises a pattern wherever it appears and keeps the parameter count manageable.
- Builds a hierarchy of features, from simple edges to abstract concepts, well suited to images.
- Assumption-light: no normality requirement, and it accepts continuous and categorical inputs.
- State-of-the-art accuracy on large labelled image and grid-structured classification tasks.
Intuition
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How it works
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When to use it
Use a CNN for classifying or predicting from images and structured grid-like data when you have a large labelled dataset — roughly 1000 or more observations — and want the model to learn features automatically rather than engineering them by hand. It does not require normally distributed data and handles continuous and categorical inputs. Below about 500 observations the network overfits and a Random Forest is safer; below about 200 the convolutional filters cannot learn meaningful patterns and SVM is preferable.
Strengths & limitations
- Learns local patterns automatically, greatly reducing the need for manual feature engineering.
- Weight sharing recognises a pattern wherever it appears and keeps the parameter count manageable.
- Builds a hierarchy of features, from simple edges to abstract concepts, well suited to images.
- Assumption-light: no normality requirement, and it accepts continuous and categorical inputs.
- State-of-the-art accuracy on large labelled image and grid-structured classification tasks.
- Needs a large labelled dataset (around 1000+ observations) to train reliably.
- On small samples (n below about 500) the network overfits and classification performance drops.
- Below roughly 200 observations the convolutional filters cannot learn meaningful patterns at all.
- Computationally demanding to train, and the learned representation is hard to interpret.
Common pitfalls
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Applications
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Frequently asked
How much data does a CNN need?
CNNs are data-hungry; a rough floor is around 1000 labelled observations. Below about 500 they tend to overfit, and below about 200 the filters cannot learn meaningful patterns — in those cases Random Forest or SVM are safer choices.
Why convolution instead of a plain neural network?
Convolution shares the same small filter across all positions of the input, so a pattern is detected wherever it appears and the parameter count stays low. A fully connected network would need vastly more weights and would not exploit the spatial structure of the data.
What does pooling do?
Pooling downsamples feature maps by summarising local neighbourhoods, which reduces spatial size, adds robustness to small shifts, and lets deeper layers build more abstract features over a wider receptive field.
What should I check before relying on a CNN?
Confirm you have a large enough labelled dataset, normalise the inputs, set up a proper train/validation/test split, address any class imbalance, and evaluate with precision, recall, F1, and a confusion matrix rather than accuracy alone.
Sources
- 1.LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.
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ScholarGate. (2026, June 1). Convolutional Neural Network. ScholarGate. https://scholargate.app/deep-learning/cnn-classification