This blog explains the Vanishing Gradient Problem in deep neural networks—why gradients shrink, how it stops learning, and proven fixes like ReLU, BatchNorm, and Residual Networks. It also covers essential strategies to improve neural network performance, including hyperparameter tuning, architecture optimization, and troubleshooting common training issues.