Neural Network Theory Laboratory

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Core Architectures
1957
First Perceptron
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Mathematical Depth
2024
Modern Architectures

Neural Network Architectures

Interactive Neural Network Lab

Select a Network Type

Select a neural network type to begin exploration

Parameters

Build Your Own Network

Drag layers from the palette to assemble a custom neural network architecture

Layer Palette

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Input
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Dense
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Conv2D
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MaxPool
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LSTM
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Attention
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Dropout
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BatchNorm
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Flatten
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Output

Network Architecture

Drag layers here to build your network

Start with an Input layer, add hidden layers, end with Output

Network Preview

Add layers to see preview

Architecture Summary

Layers: 0
Parameters (est.): 0
Status: No layers

Performance Comparison

Training Efficiency

Architecture Comparison

Performance Matrix

Research Insights

Pattern Discovery

Attention mechanisms consistently outperform recurrence in long-sequence tasks, showing 34% improvement in benchmark tests.

Hybrid Opportunity

CNN feature extraction combined with Transformer attention shows 23% improvement over single-architecture approaches.

Mathematical Foundations

Understanding gradient flow and optimization dynamics is crucial for effective neural network design and training.

Evolution Timeline