Deep Learning · TEE · section

All recovered TEE papers: unit, topic and marks map

TEE preparation and examination plan

How to use this question map

Start with the newest final and re-exam, then use older papers to practise variations. The table links each original lettered part to its complete worked answer. Repeated questions are history, not a prediction. A syllabus topic stays required even when no recovered paper asks it.

Marks: 100 marks are attempted: compulsory Q1 (20), then any four other 20-mark questions. Most recovered papers offer140 marks; NLP December2023 offers120 because the complete original ends at Q6. A mixed question retains one mark allocation; its units are not counted as separate questions. Blank individual allocations remain unknown.

2026-02-10 · re-exam · R26

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [5]	Unit 1 · ML versus DL task choices	case	Read question + answer
Q1b [5]	Unit 2 · Activation functions	theory	Read question + answer
Q1c [5]	Unit 3 · L1/L2 and specified-rate dropout	mixed	Read question + answer
Q1d [5]	Unit 5 · RNN sentiment classification	case	Read question + answer
Q2a [10]	Unit 2 · Two-output sigmoid backpropagation	numerical	Read question + answer · Loss omitted in original; answer states assumed half summed squared loss.
Q2b [10]	Unit 4 · AlexNet, Inception and architecture recommendation	case	Read question + answer
Q3a [10]	Unit 2 · Bipolar perceptron epoch	numerical	Read question + answer
Q3b [10]	Unit 4 · Five effects of CNN filter count	theory	Read question + answer
Q4a [10]	Unit 3 · Batch/SGD, learning rate and cubic-objective updates	mixed	Read question + answer
Q4b [10]	Unit 5 · LSTM and GRU; legacy bidirectional-RNN subpart	theory	Read question + answer · Current-syllabus extract; original part also contains Bidirectional-RNN advantages (iv)
Q5a [10]	Unit 5 · RNN hidden-state matrix calculation	numerical	Read question + answer
Q5b [10]	Unit 3 · RMSProp mechanism and SGD comparison	theory	Read question + answer
Q6a [10]	Unit 4 · CNN sizes and counts with binary-output ambiguity	numerical	Read question + answer · Binary head unspecified: one sigmoid or two softmax units give different parameter totals; both are stated.
Q6b [10]	Unit 2 · Sigmoid value/derivative and softmax	numerical	Read question + answer
Q7a [10]	Unit 2 · Feedforward roles and sigmoid forward pass	mixed	Read question + answer
Q7b [10]	Unit 4 · Transfer learning and its application	case	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 1: 1 parts, 5 known marks; Unit 2: 5 parts, 45 known marks; Unit 3: 3 parts, 25 known marks; Unit 5: 3 parts, 25 known marks; Unit 4: 4 parts, 40 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

Current-syllabus qualification: Q4b: mixed current and legacy tasks; the retained-only marks cannot be determined from the printed allocation. The original whole-part marks remain visible so the paper is faithfully represented.

2025-12-03 · final · F25

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [5]	Unit 1 · ML versus DL and three task choices	case	Read question + answer
Q1b [5]	Unit 2 · Loss choice and categorical cross-entropy	mixed	Read question + answer
Q1c [5]	Unit 3 · L1 sparsity, overfitting and dropout	case	Read question + answer
Q1d [5]	Unit 5 · RNN context and variable-length translation	case	Read question + answer
Q2a [10]	Unit 2 · Sigmoid network: forward pass and backpropagation	numerical	Read question + answer · Original omits inputs and loss. Answer explicitly assumes x=[1,1] and half squared loss; the result is conditional on those assumptions.
Q2b [10]	Unit 4 · ResNet; Inception versus AlexNet	theory	Read question + answer
Q3a [10]	Unit 2 · Biological neuron and perceptron epoch	mixed	Read question + answer
Q3b [10]	Unit 4 · Transfer learning for face attendance	case	Read question + answer
Q4a [10]	Unit 3 · Batch counts, momentum iterations and hyperparameters	mixed	Read question + answer
Q4b [10]	Unit 5 · Vanishing gradients and LSTM forget contribution	mixed	Read question + answer
Q5a [10]	Unit 5 · LSTM gates, cell state and hidden state	mixed	Read question + answer
Q5b [10]	Unit 3 · AdaGrad mechanism and comparison with SGD	theory	Read question + answer
Q6a [10]	Unit 4 · CNN sizes and parameters; stride-2 convolution	numerical	Read question + answer
Q6b [10]	Unit 2 · Activation functions and their roles	theory	Read question + answer
Q7a [10]	Unit 2 · Feedforward network roles and ReLU forward pass	mixed	Read question + answer · Receiving-neuron rows give 0.55; the supplied key uses a transposed convention (0.61). Both interpretations are explained.
Q7b [10]	Unit 4 · CNN feature hierarchy and applications	theory	Read question + answer

2025-02-22 · re-exam · R25

140 marks offered; 100 attempted. Combined-year archive filename contains one six-page exam, not two independent papers.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [5]	Unit 1, 4, 5 · ML versus DL and architecture choice	case	Read question + answer
Q1b [5]	Unit 3 · Training and validation errors	case	Read question + answer
Q1c [5]	Unit 4 · CNN hierarchy and stride-two convolution	mixed	Read question + answer · Padding is not specified; valid/no-padding is declared. CNN cross-correlation convention is used without kernel reversal.
Q1d [5]	Unit 5 · BPTT and recurrent strengths	theory	Read question + answer
Q2a [10]	Unit 2 · Activation, ReLU and numeric outputs	mixed	Read question + answer
Q2b [10]	Unit 5 · Vanishing/exploding gradients and GRU	theory	Read question + answer
Q3a [10]	Unit 2, 3 · 4–3–2 forward pass and dropout	numerical	Read question + answer · Activation/loss/dropout scaling missing from original; result is conditional on explicit standard classroom assumptions.
Q3b [10]	Unit 4 · Four CNN design explanations	theory	Read question + answer
Q4a [10]	Unit 4 · 64×64 CNN shape and parameter table	numerical	Read question + answer
Q4b [10]	Unit 2 · Bipolar AND perceptron	numerical	Read question + answer · Initialization, order and threshold convention explicitly supplied as assumptions; multiple equivalent separating weights exist.
Q5a [10]	Unit 4 · ResNet shortcut and Inception1×1	theory	Read question + answer
Q5b [10]	Unit 5 · ReLU RNN state with declared orientation	numerical	Read question + answer · Square matrices and printed horizontal vectors leave orientation ambiguous; both possible readings are explained, main answer follows row-vector form.
Q6a [10]	Unit 2 · 3–2–1 forward pass and update w7,w8	numerical	Read question + answer · Original does not specify loss scaling; half-SSE is declared and alternatives distinguished.
Q6b [10]	Unit 3 · AdaGrad stagnation versus RMSProp	case	Read question + answer
Q7a [10]	Unit 5 · Long-text sentiment architecture	case	Read question + answer
Q7b [5]	Unit 4 · Transfer learning	theory	Read question + answer
Q7c [5]	Unit 2, 3 · Learning rate and two losses	theory	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 1, 4, 5: 1 parts, 5 known marks; Unit 3: 2 parts, 15 known marks; Unit 4: 5 parts, 40 known marks; Unit 5: 4 parts, 35 known marks; Unit 2: 3 parts, 30 known marks; Unit 2, 3: 2 parts, 15 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

2024-02-13 · re-exam · R24

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [5]	Unit 1 · Drivers of deep learning adoption	theory	Read question + answer
Q1b [5]	Unit 2 · 10-5-5-5-4 dense network parameter count	numerical	Read question + answer
Q1c [5]	Unit 3 · Overfitting diagnosis and remedies	case	Read question + answer
Q1d [5]	Unit 4 · Max and average pooling	numerical	Read question + answer
Q2a [10]	Unit 2 · Four-hidden-neuron sigmoid MSE and update	numerical	Read question + answer
Q2b [10]	Unit 3 · Momentum/Nesterov; high momentum; excluded convex rates	theory	Read question + answer · Current-syllabus extract; original part also contains Asymptotic convergence-rate comparison on convex problems (part of i)
Q3a [10]	Unit 2 · Activation comparisons and softmax	mixed	Read question + answer
Q3b [10]	Unit 3 · Regularization importance and techniques	theory	Read question + answer
Q4a [10]	Unit 2 · Perceptron training for two epochs	numerical	Read question + answer
Q4b [10]	Unit 4 · CNN sizes, weights and biases	numerical	Read question + answer
Q5a [10]	Unit 4 · Replacing architecture to improve gradient flow	case	Read question + answer
Q5b [10]	Unit 1, 5 · X-ray DL justification; RNN pros and cons	case	Read question + answer
Q6a [10]	Unit 5 · GRU update and reset gates	theory	Read question + answer
Q6b [10]	Unit 4 · Pretrained vegetable classifier	case	Read question + answer
Q7a [10]	Unit 5 · LSTM architecture	theory	Read question + answer
Q7b [10]	Unit 5 · RNN input/output sequence architectures	theory	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 1: 1 parts, 5 known marks; Unit 2: 4 parts, 35 known marks; Unit 3: 3 parts, 25 known marks; Unit 4: 4 parts, 35 known marks; Unit 1, 5: 1 parts, 10 known marks; Unit 5: 3 parts, 30 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

Current-syllabus qualification: Q2b: mixed current and legacy tasks; the retained-only marks cannot be determined from the printed allocation. The original whole-part marks remain visible so the paper is faithfully represented.

2023-12-02 · final · F23

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [5]	Unit 1 · Deep learning features and limitations	theory	Read question + answer
Q1b [5]	Unit 2, 3 · ANN hyperparameters and significance	theory	Read question + answer
Q1c [5]	Unit 5 · Unrolled RNN and BPTT	theory	Read question + answer
Q1d [5]	Unit 4 · Convolution map	numerical	Read question + answer
Q2a [10]	Unit 2 · Binary-input step-activation network	numerical	Read question + answer
Q2b [10]	Unit 2, 1 · Activation necessity; ML/DL facial-recognition choice	mixed	Read question + answer
Q3a [10]	Unit 2 · Sigmoid output-weight backpropagation	numerical	Read question + answer
Q3b [10]	Unit 3 · Adam advantages versus SGD	theory	Read question + answer
Q4a [10]	Unit 4 · AlexNet-style layer dimensions and parameters	numerical	Read question + answer
Q4b [10]	Unit 5 · LSTM autocomplete architecture and token probabilities	case	Read question + answer
Q5a [10]	Unit 4 · Inception branches and 1x1 convolution	theory	Read question + answer
Q5b [10]	Unit 2 · Linear separability; XOR and XNOR construction	mixed	Read question + answer
Q6a [10]	Unit 5 · LSTM gates; source mixes reset/update terminology	theory	Read question + answer · Printed LSTM diagram uses update-gate wording and asks reset equations. Answer separates LSTM and GRU rather than inventing an LSTM reset gate.
Q6b [10]	Unit 4 · ResNet gradient flow and block outputs	theory	Read question + answer
Q7a [10]	Unit 5 · Paragraph sentiment model	case	Read question + answer
Q7b [10]	Unit 3 · Bias-variance tradeoff and intervention table	theory	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 1: 1 parts, 5 known marks; Unit 2, 3: 1 parts, 5 known marks; Unit 5: 4 parts, 35 known marks; Unit 4: 4 parts, 35 known marks; Unit 2: 3 parts, 30 known marks; Unit 2, 1: 1 parts, 10 known marks; Unit 3: 2 parts, 20 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

2023-01-28 · re-exam · R23

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [4]	Unit 5 · Music-generation RNN architecture	case	Read question + answer
Q1b [4]	Unit 4 · Stride-2 average pooling and purpose	numerical	Read question + answer
Q1c [4]	Unit 1 · DL advantages and limitations	theory	Read question + answer
Q1d [4]	Unit 3 · Mini-batch size and parallelism	theory	Read question + answer
Q1e [4]	Unit 2 · AND perceptron design	numerical	Read question + answer
Q2a [10]	Unit 2, 3, 4 · CNN parameters, hyperparameters and Adam	theory	Read question + answer
Q2b [10]	Unit 2, 3 · ReLU, rating regression and overfitting	case	Read question + answer
Q3a [10]	Unit 5 · RNN language model and per-token probabilities	case	Read question + answer
Q3b [10]	Unit 2 · ReLU/sigmoid forward pass and four output-weight updates	numerical	Read question + answer
Q4a [10]	Unit 4 · Small-data face-attendance transfer learning	case	Read question + answer
Q4b [10]	Unit 4 · AlexNet parameter count	numerical	Read question + answer
Q5a [10]	Unit 5 · Vanishing-gradient RNN remedies	case	Read question + answer
Q5b [10]	Unit 2 · Fraud classifier design and parameter count	mixed	Read question + answer
Q6a [10]	Unit 3 · Three optimization-failure scenarios	case	Read question + answer
Q6b [10]	Unit 4 · Convolution cost and unspecified bottleneck width	numerical	Read question + answer · The 1x1 reduction width is not specified. Answer gives a symbolic count and an explicitly assumed numerical example.
Q7a [10]	Unit 5 · LSTM sentence memory and gates	case	Read question + answer
Q7b [10]	Unit 3 · Overfitting diagnosis and remedies	case	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 5: 4 parts, 34 known marks; Unit 4: 4 parts, 34 known marks; Unit 1: 1 parts, 4 known marks; Unit 3: 3 parts, 24 known marks; Unit 2: 3 parts, 24 known marks; Unit 2, 3, 4: 1 parts, 10 known marks; Unit 2, 3: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

2022-11-19 · final · F22

140 marks offered; 100 attempted.

Question [marks]	Current unit and topic	Question form	Answer / scope
Q1a [4]	Unit 4 · Stride-2 convolution and detected feature	numerical	Read question + answer
Q1b [4]	Unit 2 · Signed gradient-descent update	numerical	Read question + answer
Q1c [4]	Unit 1 · ML and DL comparison	theory	Read question + answer
Q1d [4]	Unit 2 · Sigmoid versus tanh	theory	Read question + answer
Q1e [4]	Unit 5 · Image-captioning RNN architecture	case	Read question + answer
Q2a [10]	Unit 5 · GRU advantages and subject memory in a sentence	case	Read question + answer
Q2b [10]	Unit 2 · Two-sample sigmoid forward pass and mean-MSE backprop	numerical	Read question + answer
Q3a [10]	Unit 2, 3 · Bias/variance, four-class activation/loss and diagnosis	case	Read question + answer
Q3b [10]	Unit 4 · LeNet-style parameter count	numerical	Read question + answer
Q4a [10]	Unit 4 · Inception bottleneck computation count	numerical	Read question + answer
Q4b [10]	Unit 2, 3 · 6-5-5-4 network; AdaGrad parameters and hyperparameters	theory	Read question + answer
Q5a [10]	Unit 5 · RNN model and BPTT updates	theory	Read question + answer
Q5b [10]	Unit 3 · Optimization challenges and momentum	theory	Read question + answer
Q6a [10]	Unit 4 · CNN vanishing-gradient remedies	case	Read question + answer
Q6b [10]	Unit 2, 3 · Clothing classifier design and six diagnostic tasks	case	Read question + answer
Q7a [10]	Unit 5 · LSTM long-sequence language model	case	Read question + answer
Q7b [10]	Unit 2 · Perceptron non-linear separability and solution	theory	Read question + answer

Original offered marks by mapped topic, grouped without double counting: Unit 4: 4 parts, 34 known marks; Unit 2: 4 parts, 28 known marks; Unit 1: 1 parts, 4 known marks; Unit 5: 4 parts, 34 known marks; Unit 2, 3: 3 parts, 30 known marks; Unit 3: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

Priority without skipping the syllabus

The latest papers show the form and depth of questions, while the official current syllabus sets the learning scope. Authored drills, explanatory numbers and optional extensions are not counted as past questions. Source assumptions and ambiguous data are stated in their answers.