Why Is My Neural Network Not Learning? Debugging Tips for ML Models

If your neural network is producing poor results despite extensive training, you’re not alone. Debugging machine learning models is a crucial skill that separates beginners from experts.

Common Reasons and Fixes:

Improper Learning Rate: Too high leads to unstable training, too low results in slow convergence.
Vanishing/Exploding Gradients: Use batch normalization and activation functions like ReLU to maintain gradient stability.
Poor Weight Initialization: Consider techniques like Xavier or He initialization to avoid gradient issues.
Insufficient Data: If your dataset is too small, try using data augmentation or synthetic data generation.
Overcomplicated Model: Reduce model complexity by decreasing layers or parameters if overfitting occurs.

Machine learning is an iterative process. By methodically addressing these issues, you can fine-tune your neural network and improve its performance significantly.

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