From neurons to LLMs — demystifying the technology powering the AI revolution
By the end of this session, you'll have both conceptual understanding and hands-on experience with the foundations of AI.
Visualize how neural networks learn patterns and make decisions through layers of mathematical operations.
Understand how Large Language Models like ChatGPT are built, trained, and how they generate responses.
Identify real-world business applications where neural networks and AI drive competitive advantage.
Use interactive tools to train your own classifier and experience the AI development process firsthand.
A carefully crafted progression from concepts to hands-on building.
Opening hook with live AI demo, then dive into the brilliant 3Blue1Brown visual explanation of neural networks.
Hands-on learning with TensorFlow Playground and the stunning LLM Visualization tool.
The complete training pipeline from internet data to helpful assistant.
Stretch, grab a coffee, check your messages. Let the concepts settle.
Where AI creates value — and where it fails spectacularly.
Build your own image classifier with Google's Teachable Machine.
Key takeaways, homework assignment, and what's coming next.
The most intuitive visual explanation of neural networks ever created.
Everything you need to explore, experiment, and build.
Play with a real neural network in your browser. Adjust neurons, layers, and watch learning happen in real-time.
Explore nowStunning 3D walkthrough of GPT architecture. See every layer, attention head, and matrix operation.
Explore nowTrain your own image, sound, or pose classifier with zero code. Export to use in your projects.
Start building3.5 hour masterclass from the former Tesla AI Director. The definitive explainer for how ChatGPT works.
Watch nowSee exactly how LLMs break text into tokens. Essential for understanding prompts and context limits.
Try itFree cloud Python notebooks with GPU access. Write and run code without installing anything.
Open ColabThree interactive challenges to build your intuition.
Navigate to playground.tensorflow.org and complete these three challenges:
Classify the circular data with just 1 hidden layer. How many neurons do you need?
Why does 1 layer fail on XOR? Add layers until it works. What's the minimum architecture?
Race to get the lowest test loss! Screenshot your best result and architecture.
Test these prompts on ChatGPT, Claude, or Gemini. Document where they fail:
1. "How many r's are in the word 'strawberry'?" 2. "What is 47 × 38?" (Don't let it use tools) 3. "Spell 'encyclopedia' backwards" 4. "Count the number of words in: 'The quick brown fox jumps over the lazy dog'" 5. "Who won the Super Bowl in February 2026?"
Discussion: Why do these fail? What does this tell us about how LLMs "think"?
Use Teachable Machine to train a custom image classifier in 10 minutes:
Phone, notebook, pen — or anything around you. You need distinct categories.
Record ~30 samples per class using your webcam. Vary angles and lighting.
Click "Train Model" and test with new images. What's your accuracy?
What made classification easy or hard? How would this apply to a business problem?
Neural networks are mathematical functions that learn patterns from data through layers of simple operations.
LLMs predict the next token — they don't "know" things, they simulate plausible text based on patterns.
Business value = pattern recognition — recommendations, fraud detection, forecasting all use these same principles.
AI augments, not replaces — human oversight remains essential for verification and judgment.
Complete these tasks before our next class to prepare for prompt engineering.
Learn the art and science of getting AI to do exactly what you want. We'll write our first API calls and build a simple AI-powered application.