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Feature Map
The output of applying a convolutional filter to an input, representing detected features at various spatial locations.
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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.
- Feature Map · you are here ✓ understood
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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 Pooling A down-sampling operation in CNNs that reduces spatial dimensions while retaining important features (max pooling, average pooling). Neural Networks Receptive Field The region of input that influences a particular neuron's output, growing larger in deeper layers of CNNs. Neural Networks Filter Synonym for kernel - the learnable weight matrix applied in convolutions to extract features. Evaluation Grad-CAM Gradient-weighted Class Activation Mapping - visualizing which image regions influenced CNN predictions.