Optimizers

Optimizers are a crucial component in training neural networks, including those used in AI, Machine Learning, and Deep Learning. The main goal of an optimizer is to minimize the error or loss function of the neural network during the training process. In essence, the optimizer determines how the neural network's parameters (weights and biases) should be adjusted in order to improve the model's performance. It does this by utilizing the gradient information computed during back-propagation to guide the updates in the right direction.

Commonly used optimizers include Stochastic Gradient Descent (SGD), Momentum, and Adaptive Moment Estimation (Adam). SGD (Stochastic Gradient Descent) is a basic optimizer that updates model parameters by taking small steps in the direction of the negative gradient of the loss function. Momentum is an extension of SGD that introduces the concept of "momentum," allowing the optimizer to build up velocity in directions where gradients consistently point. Adam (Adaptive Moment Estimation) combines ideas from both SGD and Momentum while introducing adaptive learning rates for each parameter. 

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