---
title: "Deep learning, generative AI, and LLMs"
course: "AI102 · How machines learn"
unit: "Seeing problems as ML problems"
lesson: 3 of 35
source: https://open.maestrocollege.edu/how-machines-learn/deep-learning-generative-ai-and-llms
publisher: Maestro College
---

# Deep learning, generative AI, and LLMs

Lesson 3 of 35 in **How machines learn** (AI102), unit "Seeing problems as ML problems". An open lesson from Maestro College.

## What the learner will be able to do
- Understand that deep learning uses many-layered neural networks that learn from large amounts of data such as images, audio, and text.
- Understand that generative AI produces new content, and that an LLM is a generative model trained on huge amounts of text to predict the next token (a word or part of a word).
- Identify a real product and a typical use case for deep learning, generative AI, and LLMs, and identify when a simple ML model fits better.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.

## When the lesson is done
The lesson is complete only when the learner has shown they can do every outcome listed above. Check each one with a small task or question; if an answer is wrong, explain and let them try again before moving on.
When every outcome is covered:
1. Say clearly: "Lesson 3 complete: Deep learning, generative AI, and LLMs."
2. Give a two-line recap of what they learned.
3. Name the next lesson, "Features, labels, and examples" (lesson 4 of 35), and give the learner its link: https://open.maestrocollege.edu/how-machines-learn/features-labels-and-examples.md. Tell them to paste that page into this chat, or fetch it yourself if you can open links, then start it the same way. Do not start teaching the next lesson's topic until the learner has it.

Previous lesson: Where ML fits in the AI map
Next lesson: Features, labels, and examples