Practical Neural Network Training

Interactive training dashboard with real-time monitoring and hyperparameter tuning

Training Progress & Loss Curves

Training Loss (Blue) vs Validation Loss (Red) - Watch for overfitting!

Ready to train
Epoch: 0 / 10
Hyperparameters
Regularization
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Training Loss
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Validation Loss
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Training Accuracy
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Validation Accuracy
Overfitting Detection

Gap between training and validation indicates overfitting

Training Tips

Overfitting: If validation loss increases while training loss decreases, you're overfitting. Try reducing model complexity or increasing regularization.

Underfitting: If both losses are high, the model is underfitting. Try increasing model capacity or training longer.

Learning Rate: Start with a moderate learning rate and adjust based on training stability.

Hyperparameter Tuning Guide

Learning Rate

Too High: Training becomes unstable, loss oscillates

Too Low: Training is very slow, may get stuck in local minima

Optimal: Loss decreases steadily without oscillation

Batch Size

Small: More noisy gradients, better generalization

Large: Stable gradients, faster training

Optimal: Balance between stability and generalization

Regularization

Dropout: Randomly deactivates neurons during training

L2: Penalizes large weights, encourages smaller weights

Early Stopping: Stop training when validation loss stops improving

Architecture

Too Small: Underfitting, high bias

Too Large: Overfitting, high variance

Optimal: Just enough capacity for the task