Deep Learning · TEE · section
Practice resources
TEE preparation and examination plan
The reading flow places worked numericals and relevant examination questions beside their concepts. Solved Questions also provides separate lists of practice numericals, past examination questions and theory answers. Every question has its answer directly below it.
The supplied historical papers are filtered against the current syllabus. The TEE mock uses the December 2025 paper as a three-hour rehearsal with full solutions. This is historical practice; it is not a prediction of the forthcoming paper.
How the supplied papers ask questions
The course policy allocates 10 marks to Class Test 2. The shared confirmed pattern is Q1(a,b), 2 marks each compulsory, followed by any two of Q2–Q4, 3 marks each, in one hour. The faculty message relayed on 12 October identifies Unit 3 and Unit 4 theory and numericals, highlighting two Unit 4 numerical questions. It describes alternative theory/numerical combinations without fixing their exact question numbers. Use the MT2 route for that focused preparation; the TEE route covers all five units.
TEE has 100 marks in three hours. Q1 is compulsory, then answer any four of Q2–Q7. Each main question carries 20 marks. The supplied 2023–2026 end papers use four 5-mark parts in Q1 and two 10-mark parts in later main questions. The older 2022/January 2023 papers use five 4-mark parts in Q1. This is an observed historical change, not a prediction of the next paper's subparts.
| Asked question family | Examples in the supplied papers | What the answer must contain |
|---|---|---|
| Neuron / activation calculation | MT1A Q2–Q3 (3 marks each); R26 Q6b (10 marks, mixed calculation and explanation) | Matching products, bias once, activation, requested derivative/probabilities |
| Forward pass and backpropagation | MT1A Q4 (3); F25 Q2a, R26 Q2a, R24 Q2a (10 each) | Network data and loss convention, forward values, chain-rule factors, requested gradients and updates |
| Perceptron training | F25 Q3a, R26 Q3a, R24 Q4a (10 each) | Threshold and target convention, every sample in order, update only as required, final weights |
| Optimizer and update-count questions | F25 Q4a and R26 Q4a (10 each) | Differentiate when needed, initialize state, show each iteration; recompute or use supplied gradients correctly |
| Regularization / overfitting | R26 Q1c (5); R24 Q3b and R23 Q7b (10 each) | Definition, mechanism, equations where relevant, training/inference distinction, suitable remedy |
| CNN map / shape / parameter calculations | F25 Q6a, R26 Q6a, R24 Q4b (10 each) | Every map entry if requested, each layer shape, filter weights and biases, pooling zero parameters, dense count |
| Architectures and comparison | F25 Q2b, F23 Q5a, R23 Q4a (10 each) | Labelled structure, working mechanism, reason for design, advantages and limitations |
| Recurrent state / LSTM / GRU | R26 Q5a, F25 Q5a, R24 Q6a (10 each) | State/gate definitions, correct activation, carried state, requested calculations and comparison |
These are examples of question types, not an official marking rubric or an assurance that topics repeat. The TEE route retains 108 mapped question parts across the two MT1 sets and six end-semester papers. Two historical parts are restricted to their current-syllabus content. A supplied model answer does not override an explicit loss, dimension or convention in the question.