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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