Deep neural networks, inspired by human brain processing, are powerful but prone to overfitting, which hinders their ability to generalize to new data. To address overfitting, techniques such as regularization, including L1 or L2 normalization and dropout layers, are recommended. Hyperparameter tuning, involving the adjustment of parameters like activation functions, learning rates, and batch sizes, is crucial for optimizing model performance. The quality and quantity of data are vital, as poor data can degrade model performance, emphasizing the need for techniques like data augmentation. Ensemble algorithms, which combine multiple predictors, often outperform individual models and are frequently used in competitions. Establishing a baseline model is critical in the iterative process of developing effective deep networks, providing a reference point for improvement and context for evaluating a model's performance.