Training a neural network is fundamentally an optimization problem. This blog explains the role of optimizers in deep learning, how gradient descent works, its batch, stochastic, and mini-batch variants, and why challenges like learning rate sensitivity, local minima, and saddle points motivate advanced optimization techniques.

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.