🔬 Overview: Understanding Neural Networks
What are Neural Networks?
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes (neurons) that process information and learn patterns from data.
🧠 Biological Neuron
Cell Body
Receives signals through dendrites, processes in cell body, sends output via axon
⚡ Artificial Neuron
x₁
x₂
x₃
Σ → f()
y
Takes weighted inputs, sums them, applies activation function, produces output
📊 Neural Network History Timeline
🏗️ Build Mode: Construct Your Network
🧰 Layer Toolbox
Input Layer
Dense Layer
Conv Layer
RNN Layer
Output Layer
🎯 Network Architecture
Drag layers here to build your network
⚙️ Network Parameters
Select a layer to configure parameters
🎓 Training Simulator
📋 Training Configuration
📈 Training Progress
Current Epoch:
0
Training Loss:
-
Accuracy:
-
🎨 Visualization Hub
➡️ Forward Pass Animation
Watch data flow from input to output through network layers
⬅️ Backward Pass Animation
See how gradients propagate back to update weights
🎯 Confusion Matrix
Visualization of model prediction accuracy
📊 Activation Functions
🧩 Quiz & Resources
🎯 Knowledge Quiz
Question 1 of 3
Loading question...
Score: 0/0
📚 Learning Resources
📖 Glossary
Activation Function
Backpropagation
Epoch
Loss Function