Deep learning, a subset of machine learning, utilizes complex multi-layer neural networks to extract information from data, building on the foundational Perceptron model developed in 1957 by Frank Rosenblatt. Modern deep learning models, while powerful, require significant computational resources and time for training. Techniques such as optimization algorithms, transfer learning, and early stopping can significantly reduce training time. The Adam optimizer is particularly noted for its versatility and effectiveness in adjusting learning rates during training. Transfer learning leverages pre-trained models to save resources by only modifying the output layer for specific tasks. Early stopping helps prevent overfitting by halting training once improvements plateau. Training on GPUs, especially through cloud services like Google Colab and Kaggle, offers a substantial speed advantage, though access can be costly. By employing these methods, the efficiency and performance of deep learning models can be greatly enhanced.