🧠 Neural Network: Before vs After Training

See the magic of machine learning - how a random network becomes intelligent!

1
Choose Problem
2
Test Untrained
3
Train Network
✓
See Results!

🎯 Step 1: Choose Your Problem

Select a classification problem for the neural network to learn:

💡 Current Problem: XOR

The XOR problem is a classic test - the network needs to learn that points in opposite corners belong to the same class. This requires understanding non-linear patterns!

🔬 Step 2: Test the Network

❌ Before Training (Random Weights)

🤔 Random guess: Click "Test" to see
Confidence: 50%

✅ After Training (Learned Weights)

🎯 Trained prediction: Train first!
Confidence: 0%

📊 Performance Metrics

Accuracy Before

~50%
Random guessing

Accuracy After

0%
Needs training

Loss Before

High
Very confused

Loss After

--
Much better!
🎮 Go to Full Training Visualizer

🤔 What's Happening Here?

Before Training:

  • The network has random weights - like a newborn baby's brain
  • It makes random guesses - about 50% accurate (coin flip)
  • The decision boundary is chaotic and meaningless
  • It has no understanding of the pattern

After Training:

  • The network has learned weights through backpropagation
  • It makes intelligent predictions - often 95%+ accurate
  • The decision boundary matches the actual pattern
  • It understands the underlying structure

🔧 How Training Works

1️⃣ Forward Pass

Input flows through the network, producing a prediction

2️⃣ Calculate Error

Compare prediction with the correct answer

3️⃣ Backward Pass

Adjust weights to reduce error next time

4️⃣ Repeat

Do this thousands of times until it learns!