Neural network training presents challenges due to numerous hyperparameters requiring careful tuning, with the learning rate being particularly crucial for model performance. The article explores the importance of learning rate scheduling and various strategies to adjust the learning rate during training to improve neural network optimization. It discusses constant learning rates, learning rate decay, and custom scheduling techniques such as linear, time-based, exponential, and step-based decays, which can be implemented in Keras using Neptune.ai for experiment tracking. The analysis highlights that selecting an appropriate learning rate schedule is key to achieving effective model convergence, as overly aggressive decay can prevent reaching minima while slow decay may lead to erratic updates. Additionally, the article covers adaptive optimizers like Adam, noting that while they are popular, they may not always be the best choice without proper hyperparameter tuning. The findings emphasize the need to balance learning rate adjustments with other hyperparameters for optimal neural network training.