Deep learning model training is fundamentally an optimization problem, where various algorithms adjust model parameters to minimize a loss function. The primary method, Gradient Descent, iteratively updates model weights based on the gradient of the cost function, but faces challenges like local minima and learning rate selection. To improve efficiency and convergence, variations like Stochastic Gradient Descent (SGD), Mini-batch Gradient Descent, AdaGrad, RMSprop, AdaDelta, and Adam have been developed. Each algorithm offers unique strategies to address specific issues of basic Gradient Descent, such as adapting learning rates or managing computational resources for large datasets. For instance, AdaGrad adjusts learning rates based on historical gradients but suffers from diminishing rates over time, which RMSprop and AdaDelta address by maintaining a consistent pace of learning. Adam combines features of AdaGrad and RMSprop, providing adaptive learning rates and bias correction, making it suitable for a wide range of deep-learning tasks. Understanding these algorithms' strengths and weaknesses is crucial for selecting the most appropriate method for specific deep learning projects.