Neural Network Training
Ever wondered how AI actually learns? Start here! Watch a neural network begin with completely random weights (knowing nothing) and gradually learn to predict student test performance through training. You'll see the network make mistakes, calculate errors, and adjust its weights to improve - just like how you learn from practice tests. This is backpropagation and gradient descent in action, the foundation of all modern AI!
What You'll Learn
š Why Watch This First?
This visualization shows HOW neural networks learn. You'll see the network start with random weights (knowing nothing) and gradually learn patterns through training. This makes the "Neural Network Forward Pass" much more meaningful - you'll understand where those weights came from!
Prerequisites
What you need to know (spoiler: not much!)
- Basic understanding of trial-and-error learning
- No math background needed!
Interactive Visualization
šÆ The Challenge: Can a Computer Learn Like You Do?
Think about how you learned to recognize patterns. Maybe you noticed that students who study more tend to get better grades. Or that getting enough sleep helps with test performance.
Here's the amazing part: We're going to watch a computer figure out these same patterns, completely on its own!
The Setup:
- ā¢We have a "brain" (neural network) that starts knowing absolutely nothing
- ā¢We'll show it examples of students and whether they passed or failed
- ā¢It will try to guess the pattern, make mistakes, and gradually get better
Just like learning to ride a bike: At first you wobble and fall, but each attempt teaches you something. Soon you're riding smoothly!
The network you see has three "senses" (inputs) and two "opinions" (outputs). Right now, it's like a newborn - all potential, no knowledge.
Neural Network State
Real-time calculation with actual math
Key Takeaways
š² Starting from Zero
AI doesn't start with knowledge - it begins with random weights. All intelligence is learned through training!
š Learning = Pattern Recognition
The network discovered that study time, sleep, and past performance predict success - without being told these rules.
š Trial and Error Works
Like learning to ride a bike, the network makes mistakes, adjusts, and gradually improves. This is backpropagation!
āļø Gradual Improvement
Each training example slightly adjusts the weights. After many examples, these small changes add up to intelligence.
š Universal Process
This exact process trains ChatGPT, self-driving cars, image recognition, and all modern AI. Scale up the data and network size!
Next Steps
Continue your learning journey
Now that you understand training, see the trained network in action:
Neural Network Forward PassOr continue to learn how networks optimize their learning process:
Gradient Descent Optimization