🧠 Vanishing Gradient Problem: Interactive Learning Lab

🎯 Key Question: Why do deep neural networks train so slowly? Let's discover the answer through interactive experimentation!
0.01
4 layers
1
🔴 Deep Network HIGH IMPACT
1.0 → 0.5 → 0.25 → 0.125 → 0.06
Ready to train
Epoch: 0
🟡 Wide Network MEDIUM IMPACT
1.0 → 0.5 → 0.25 → 0.125 → 0.06
Ready to train
Epoch: 0
🟢 Skip Connections PROBLEM SOLVED
1.0 → 0.9 → 0.8 → 0.7 → 0.6
Ready to train
Epoch: 0

📊 Real-Time Comparison: Gradient Magnitudes

Network Type First Layer Gradient Last Layer Gradient Training Status Convergence
Deep Network - - Not Started -
Wide Network - - Not Started -
Skip Connections - - Not Started -

❌ Common Misconception

"If my network isn't learning, I should add more neurons!"

Watch the Wide Network above - it has more neurons but still suffers from vanishing gradients. Adding width doesn't solve the core problem of gradient flow through depth.

✅ Key Insight

Architecture Design > Network Size

Skip connections allow gradients to flow directly to earlier layers, maintaining their magnitude. This is why ResNet, DenseNet, and other modern architectures work so well!

🎯 Learning Progress

Understanding: 0%

Try different settings and observe how gradients behave. Each experiment increases your understanding!

🚀 Experiment Ideas:

1. Try increasing learning rate - does it solve vanishing gradients?

2. Make the network deeper - what happens to early layer gradients?

3. Add more neurons per layer - does training improve?

4. Compare skip connections vs. regular networks at the same depth