An interactive web-based visualization tool that demonstrates the performance differences between SGD with Momentum and Adam optimization algorithms in real-time.
This interactive visualization demonstrates the key differences between two popular optimization algorithms used in deep learning:
- SGD with Momentum - Traditional optimization with velocity accumulation
- Adam (Adaptive Moment Estimation) - Adaptive learning rate method with momentum
The tool allows users to experiment with different hyperparameters and observe how they affect training dynamics in real-time.
- Interactive Controls: Adjust learning rates and momentum parameters in real-time
- Live Visualization: Watch training progress with animated charts
- Real-time Comparison: Side-by-side comparison of both optimizers
- Dual Metrics: Track both loss and accuracy simultaneously
- Mathematical Formulas: Clear display of the underlying algorithms
- Comparison Table: Key differences between optimizers at a glance
- Reset Functionality: Easily restart simulations with new parameters
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Adjust Hyperparameters:
- SGD Learning Rate (α): Typically higher (0.01-0.5)
- Adam Learning Rate (α): Typically lower (0.0001-0.01)
- Momentum (β): Controls gradient history (0.5-0.99)
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Start Training: Click "Start Training" to begin the simulation
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Observe Results: Watch how loss decreases and accuracy improves over epochs
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Experiment: Try different parameter combinations and observe the effects
- Adam: Faster initial convergence, may plateau earlier
- SGD: Slower but more steady progress, often better final generalization
- High Learning Rates: Faster learning but potential instability
- Low Learning Rates: Stable but slower convergence