Convolutional Neural Networks (CNNs)
CNNs are specialised neural networks designed to process grid-like data, particularly images. They use convolutional layers that apply filters across the input to detect features like edges, textures,…
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Definition
CNNs are specialised neural networks designed to process grid-like data, particularly images. They use convolutional layers that apply filters across the input to detect features like edges, textures, and patterns. CNNs power image recognition, object detection, facial recognition, and medical image analysis. Architectures like ResNet, VGG, and EfficientNet have achieved superhuman performance on many visual tasks.
Key Points
- Convolutional layers detect local features; pooling layers reduce dimensionality
- Transfer learning: use pre-trained models (ImageNet) and fine-tune for your task
- Applications: image classification, object detection, facial recognition, medical imaging
- Can be deployed on edge devices (mobile phones, cameras) for real-time processing
Related Terms
Frequently Asked Questions
Transfer learning uses a CNN pre-trained on a large dataset (like ImageNet with 14M images) as a starting point for your specific task. Instead of training from scratch, you fine-tune the last few layers on your data. This dramatically reduces the data and compute needed — you might need only 100-1,000 images instead of millions.
Yes. Lightweight architectures like MobileNet and EfficientNet are designed for mobile and edge deployment. Using frameworks like TensorFlow Lite or ONNX Runtime, CNNs can run at 30+ FPS on modern smartphones. This enables offline-capable applications for Indian users with limited connectivity.
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