Module Check: Deep Learning in the Real World
You have covered ten lessons and one complete arc: how deep networks are built, what they can do with images, language, and generation, why they became possible when they did, what they cost, where they fail, and how to use them responsibly. This final lesson consolidates the whole module. Work through each section carefully — this is not a formality, it is how knowledge becomes durable.
Flashcards — click each card to reveal the answer
What does the 'depth' in deep learning refer to?
A CNN learns to recognize faces regardless of whether the face is in the center or the corner of the image. Which property makes this possible?
Which three ingredients combined in the early 2010s to make deep learning practically viable?
A company trains a hiring algorithm on ten years of historical hiring data. Historically, 80% of people hired into engineering roles were men. What failure mode is most likely?
What is the most important distinction between training cost and inference cost for a large language model?
You ask an AI assistant for the average temperature in a city and it gives you a specific number with no hesitation. What should you do before using that number?
Deep learning works by stacking many layers of learned transformations, each building on the last. CNNs use this to see; transformers use it to understand language; generative models use it to create. This became practical around 2012 because large datasets, GPU hardware, and better algorithms arrived together. It comes with real financial and environmental costs that concentrate power in a few organizations. It fails in specific, predictable ways: brittleness to distribution shift, bias from training data, hallucination in language models, and adversarial vulnerability. Responsible use requires human oversight scaled to the stakes, diligent verification, and honesty about limits.
Module Synthesis: Design an AI-Powered School Tool
- Design an AI-powered tool for your school. It can do anything you think would genuinely help students or teachers.
- Write a one-page design document with five required sections:
- 1. What the tool does and which type of deep learning architecture it uses (CNN, transformer, generative model, or combination — explain why).
- 2. What training data it would need and where that data would come from.
- 3. Which failure mode from Lesson 7 is the biggest risk for your tool and why.
- 4. Your oversight protocol: at least three specific steps to keep humans appropriately involved.
- 5. What you would tell users honestly about what the tool can and cannot do.
- Present your design to the class. Vote on: which design is most genuinely useful, and which oversight protocol is most thorough.