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Receptive Field
The region of input that influences a particular neuron's output, growing larger in deeper layers of CNNs.
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3 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Neural Network ✓ understood
A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.
- Convolution ✓ understood
A mathematical operation that applies filters/kernels to input data to extract features like edges, textures, and patterns.
- Receptive Field · you are here ✓ understood
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Neural Networks Kernel A small matrix of weights used in convolutional layers to detect specific features or patterns in input data. Neural Networks Stride The step size by which a convolutional filter or pooling window moves across the input. Neural Networks Dilated Convolution Convolution with gaps between kernel elements, expanding the receptive field without increasing parameters. Neural Networks Convolutional Neural Network A neural network that scans images with small learned filters, reusing the same weights at every position to build up from edges to whole objects. Neural Networks Feature Map The output of applying a convolutional filter to an input, representing detected features at various spatial locations.