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Neural Network Pro
Interactive multi-layer neural network simulator with comprehensive features
📊 Network Architecture
Input: 3→Layer 1: 4→Layer 2: 1→Output: 1
Total parameters: 21
Input Layer
3 valuesx1:
x2:
x3:
Learning Rate
For training (reference parameter)
Network Output
Layer 1 Output(relu)
n1:0.450000
n2:0.000000
n3:0.420000
n4:0.210000
Layer 2 Output(sigmoid)
n1:0.619399
Final Network Output
Output 1:
0.619399
Hidden & Output Layers
Layer 1
Neuron 1 (Weights & Bias)
Weights:
Bias:
Neuron 2 (Weights & Bias)
Weights:
Bias:
Neuron 3 (Weights & Bias)
Weights:
Bias:
Neuron 4 (Weights & Bias)
Weights:
Bias:
Layer 2(Output Layer)
Neuron 1 (Weights & Bias)
Weights:
Bias:
💡 Quick Tips
- • Click "Add Layer" to create deeper networks (default: 3 neurons with ReLU)
- • Typical architecture: Input → Hidden Layer(s) with ReLU → Output with Sigmoid/Linear
- • Adjust neurons per layer to control network capacity
- • Hidden layers: Use 4-10 neurons with ReLU activation
- • Output layer: Use Sigmoid for binary, Linear for regression, or multiple neurons for multi-class
- • Randomize weights to experiment with different initializations
- • Expand neuron details to fine-tune individual weights and biases
🏗️ Building Real Networks
For Binary Classification:
- • Hidden: 4-8 neurons (ReLU)
- • Output: 1 neuron (Sigmoid)
- • Example: [2, 3] → 6 → 4 → 1
For Multi-Class:
- • Hidden: 6-10 neurons (ReLU)
- • Output: N neurons (Linear/Sigmoid)
- • Example: [4] → 8 → 6 → 3
For Regression:
- • Hidden: 4-8 neurons (ReLU)
- • Output: 1 neuron (Linear)
- • Example: [3] → 6 → 4 → 1
Deep Networks:
- • Multiple hidden layers
- • Gradually reduce neurons
- • Example: [5] → 8 → 6 → 4 → 1
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
Deep Learning
CNN
Convolutional neural networks for image and spatial data
Deep Learning
RNN
Recurrent networks for sequential and time-series data
Deep Learning
LSTM
Long short-term memory networks for long sequences
Deep Learning
Transformer
Attention-based architecture for modern NLP tasks
Optimization
Gradient Descent
Backpropagation and optimization for neural networks
Deep Learning
Autoencoder
Unsupervised learning for dimensionality reduction