MegaMaester

Artificial Intelligence · Module 4

Deep Learning

Module 2 explained classical machine learning; Module 3 showed what generative AI can do. This module opens the engine that powers almost all of modern AI: deep learning — layered neural networks that learn from data.

You will learn what deep learning is and why it took off, what is actually inside a neural network, how such networks learn, how they see images and process language, where they fail, and where they show up in the world — all in plain language, without heavy mathematics.

Lessons
7 lessons
Estimated time
~6-8 hours
Assessment
Module quiz included

Not started yet

Lessons

Lesson 1 · 16 min

What Is Deep Learning?

Lesson 2 · 16 min

Inside a Neural Network

Lesson 3 · 16 min

How Neural Networks Learn

Lesson 4 · 16 min

Deep Learning for Images

Lesson 5 · 16 min

Language and Sequence Models

Lesson 6 · 16 min

The Limits of Deep Learning

Lesson 7 · 16 min

Deep Learning in the World

Check your understanding

Finish the lessons, then test yourself. Your best score is saved on this device. You can also open this quiz on its own page.

Deep Learning — Module Assessment

14 questions · pass mark 75%

  1. 1.What best describes the difference between deep learning and much of classical machine learning?
  2. 2.Which pair of developments most directly enabled the recent rise of deep learning?
  3. 3.What does an artificial neuron do with its inputs?
  4. 4.Who introduced the perceptron, an early model of an artificial neuron, and when?
  5. 5.During training, what is the direct purpose of the loss?
  6. 6.What does backpropagation actually do after the loss is calculated?
  7. 7.In a convolutional neural network, how does understanding of an image typically build up across the layers?
  8. 8.Why is AlexNet's result at the 2012 ImageNet competition considered a turning point?
  9. 9.What problem with recurrent neural networks did attention most directly help solve?
  10. 10.Which statement best describes what the transformer architecture changed about processing sequences?
  11. 11.What is an adversarial example in deep learning?
  12. 12.Why is the 'black-box' nature of deep learning a concern in high-stakes settings like medicine?
  13. 13.Which statement best describes what AlphaGo's 2016 victory over Lee Sedol demonstrated?
  14. 14.Why does a deep-learning model that scores well on a benchmark sometimes fail in the real world?
Answer every question to submit.