Overview of various Optimizers in Neural Networks

Optimizers are algorithms or methods used to change the attributes of the neural network such as weights and learning rate to reduce the losses. Optimizers are used to solve optimization problems by minimizing the function. How you should change your weights or learning rates of your neural network to reduce the losses is defined by the optimizers you use. Optimization algorithms are responsible for reducing the losses and to provide the most accurate results possible. The weight is initialized using some initialization strategies and is updated with each epoch according to the update equation. The above equation is the update equation using which weights are updated to reach the most accurate result.