Convolutional Neural Network
CNN
Neural network architecture designed for processing grid-like data such as images. Uses convolution operations to automatically learn spatial features and patterns.
In-Depth Explanation
Convolutional Neural Networks (CNNs) revolutionised computer vision by learning to recognise visual patterns directly from pixels. They remain fundamental to image processing AI.
CNN key components:
- Convolutional layers: Learn local patterns (edges, textures)
- Pooling layers: Reduce spatial dimensions, add invariance
- Fully connected layers: Combine features for final output
- Activation functions: Add non-linearity (ReLU)
Why convolutions work for images:
- Local connectivity: Pixels near each other are related
- Weight sharing: Same filter across entire image
- Translation invariance: Recognise patterns anywhere
- Hierarchical features: Simple → complex patterns
Landmark CNN architectures:
- LeNet (1998): Pioneer for digit recognition
- AlexNet (2012): ImageNet breakthrough
- VGG (2014): Deeper is better
- ResNet (2015): Skip connections enable very deep nets
- EfficientNet (2019): Balanced scaling
Business Context
CNNs are the workhorse of image AI. From quality inspection to document OCR, most visual AI applications use CNN-based architectures or their derivatives.
How Clever Ops Uses This
We use CNN-based models for Australian business applications including document processing, visual inspection, and image classification tasks.
Example Use Case
"A ResNet-based model classifying medical images, trained on hospital data to assist radiologists in detecting anomalies."