Deep Learning · TEE · section
R26 Q1a
Re examination February 2026 fully solved
Question
Question (5 marks). Describe two situations favoring DL over traditional ML. Choose ML or DL for house prices from 1,000 numerical records, real-time facial-emotion video recognition, and translation from millions of sentence pairs.
Answer — step by step
Deep learning is a branch of machine learning that learns representations using neural networks with multiple processing layers. Machine learning is itself a branch of artificial intelligence. In image recognition, early CNN layers can detect local edges, later layers combine patterns into textures and parts, and deeper layers support object recognition.
| Comparison | Traditional ML approaches | Deep learning approaches |
|---|---|---|
| Features | Often depend on manually designed features | Often learn features jointly with the prediction task |
| Data | Often strong baselines for smaller structured datasets | Training large models from scratch often benefits from abundant data |
| Computation | Many methods can train economically on CPUs | Large networks often need substantial compute and memory |
| Interpretability | Some models such as small decision trees are easier to inspect | Large multilayer representations can be harder to explain |
These are tendencies, not rules. A pretrained network can be useful with a small target dataset, and boosted trees can outperform deep networks on some tabular problems. Choose using data type, size, available pretrained models, resources and held-out performance.
Drivers of deep learning include improved hardware, large datasets, automatic differentiation, improved activations/optimization and useful architectures. Advantages include automatic feature learning and strong modeling of complex image, audio and sequence patterns. Limitations include data and compute requirements, overfitting, sensitivity to training choices, and difficulty explaining decisions. Applications include image classification, speech recognition, translation and medical-image analysis.
Choices: house prices: start with tabular ML because the features are structured and data are limited. Video emotion: DL can learn visual spatial features and temporal patterns. Large parallel-text translation: DL can learn representations and sequence dependencies. Two situations particularly suited to learned deep representations are complex image recognition and large-scale speech/language tasks; compare validation results instead of assuming an unconditional advantage.