The article explores seven common optimization methods used in deep learning, emphasizing the importance of optimization in enhancing machine learning and deep learning algorithms. It starts with a detailed explanation of gradient descent, a first-order iterative method crucial for understanding machine learning. The article discusses variations like mini-batch and stochastic gradient descent, which address the slow learning issues associated with large datasets. Momentum is introduced as a technique to reduce oscillations in gradient steps by incorporating historical data, although it may sometimes overshoot the minimum value. Nesterov's method is presented as an enhancement, offering better convergence by predicting the gradient's future position. Adaptive gradient techniques, such as Adagrad, Adadelta, and RMSprop, adjust learning rates based on parameter update frequencies and past gradients. Adam, a widely used optimizer, combines features of RMSprop and momentum, using exponentially decaying averages of past gradients and squared gradients to achieve efficient learning. The article concludes by recommending further reading for a deeper understanding of these optimization techniques.