Deep Learning · TEE · section
Rehearsal Q1a
Complete TEE paper rehearsal
Question (5 marks). Give four ML versus DL differences with examples. Choose and justify ML or DL for (1) 2,000 short product reviews, (2) labeled financial fraud records, (3) captions for 100,000 labeled natural-scene images.
Complete worked answer.
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: (1) Start with traditional ML using text features because the labeled dataset is small; pretrained text models are a viable extension. (2) Start with a tabular ML classifier such as a tree ensemble; labeled structured features do not automatically require DL. (3) DL is appropriate for learning visual features and generating a word sequence; a CNN encoder with a sequence decoder is a syllabus-aligned design. Compare held-out metrics rather than claiming one method must always win.