Generated by claude-sonnet-4-6 · 2026-06-21 03:19 · grounding rag
Model Answer
(A) CNN and ANN:
CNN – Convolutional Neural Network: CNN is a Deep Learning algorithm that takes an input image, assigns importance (learnable weights and biases) to various aspects/objects in the image, and differentiates one from the other. It consists of layers: Convolution Layer, ReLU, Pooling Layer, and Fully Connected Layer.
ANN – Artificial Neural Network: ANN is a computational model inspired by the human brain, consisting of interconnected nodes (neurons) organised in layers that process information to learn patterns and make decisions.
(B) Identification of Layers:
- Box 1 → Hidden Layer (the intermediate processing layer between input and output)
- Box 2 → Output Layer (produces the final prediction/result)
Source: Chapter 5, Section 5.6 Convolutional Neural Networks (CNN)
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Explanation
- For (A), the examiner expects the full form + a clear definition for each term. CNN's definition is directly from the textbook (Section 5.6); ANN is a prerequisite concept — keep it concise but accurate.
- For (B), in a standard feedforward neural network diagram, the layers in order are: Input Layer → Hidden Layer(s) → Output Layer. Since Box 1 and Box 2 are the unlabelled middle and last layers respectively, they correspond to Hidden Layer and Output Layer. Always name them precisely — partial credit may be lost for vague answers.
- Do not expand on CNN layers in sub-question (A) beyond what is necessary; save word count as this is only 3 marks.