Module Check: Modern Deep Learning
You have covered the full arc of modern deep learning: how convolutional networks exploit spatial structure in images; how recurrent networks handle ordered data and where they break down; how the attention mechanism breaks the RNN bottleneck and why the Transformer became the dominant architecture; what it takes to train large models across many machines; how to transfer pretrained knowledge efficiently; how to evaluate models without deceiving yourself; how to get models into production and keep them working; and where deep learning genuinely fails. This review consolidates all of it — not just by recalling facts, but by reasoning across the module.
Key Concepts Review
Flashcards — click each card to reveal the answer
Module Quizzes
A 3×3 convolutional filter is applied to a 256×256 grayscale image. Approximately how many multiplications does one pass of this filter require?
An LSTM and a Transformer are both trained to translate 300-word documents. The LSTM trains 10x faster per epoch. Why might the team still choose the Transformer for this task?
A team fine-tunes a pretrained image classifier on 200 medical images of a rare bone abnormality. They achieve 95% accuracy on a held-out set of 40 images. What is the most important caveat about this result?
A deployed sentiment model reports 92% accuracy in production monitoring. Three months later, without any model change, accuracy drops to 78%. What is the most likely explanation?
Why does the self-attention computation scale as O(n^2) in sequence length n?
A researcher reports that their new model achieves 98.7% accuracy on ImageNet, surpassing the previous best by 1.2%. A critic argues this result may not be meaningful. Which of the following is the strongest critique?
Synthesis
Every architecture in this module is a different answer to the same question: how do we structure computation so that the patterns that matter for a task are easy to learn from data? CNNs structure computation around local spatial neighborhoods. RNNs structure it around temporal sequences. Transformers structure it around content-based pairwise relevance. Scale and transfer learning amplify whichever structure fits the problem. Evaluation and monitoring ensure the structure keeps working after deployment. And the limits remind us that no structure currently known is sufficient for all problems.
Capstone: Design and Defend a Deep Learning System
- Step 1. Choose one of the following deployment scenarios:
- (a) An app that listens to a student reading aloud and identifies mispronounced words in real time, on a smartphone.
- (b) A system that reviews satellite imagery daily and flags fields at risk of crop failure for agricultural insurance underwriters.
- (c) A tool that assists emergency dispatchers by summarizing the key facts from 911 call transcripts in under two seconds.
- Step 2. Apply the full module framework:
- - Architecture: which architecture fits the input structure, relevant relationships, output, and constraints? Name it precisely.
- - Training: how much data exists or could be collected? Would you pretrain, fine-tune, or train from scratch? Which PEFT method if any?
- - Evaluation: which metrics matter? What does a false positive and false negative cost in this context? What would your test set look like?
- - Deployment: what serving latency is required? How would you monitor for drift? What would trigger retraining?
- - Limits: identify the single most dangerous failure mode for your system in the real world.
- Step 3. Write a structured one-page system brief covering all five areas. Be specific — name architectures, metrics, and monitoring strategies by name.
- Step 4. Exchange briefs with a partner. Write two concrete technical objections to their proposed system. Respond to their objections in writing.
- Step 5. Revise your brief based on the strongest objection you received.