What is Optimizer?

What is Optimizer ? It is very important to tweak the weights of the model during the training process, to make our predictions as correct and optimized as possible. But how exactly do you do that? How do you change the parameters of your model, by how much, and when? Best answer to all above question is optimizers.

What is Adam optimizer?

Adam optimizer is one of the most popular and famous gradient descent optimization algorithms. It is a method that computes adaptive learning rates for each parameter. It stores both the decaying average of the past gradients , similar to momentum and also the decaying average of the past squared gradients , similar to RMS-Prop and Adadelta.

What are optimizers in neural network?

Optimizers are mathematical functions which are dependent on model’s learnable parameters i.e Weights & Biases. Optimizers help to know how to change weights and learning rate of neural network to reduce the losses. This post will walk you through the optimizers and some popular approaches. Types of optimizers

What is AdaGrad optimizer?

Frequent alteration of model parameters. If Momentum is used then helps to reduce noise. AdaGrad stands for Adaptive Gradient Algorithm. AdaGrad optimizer modifies the learning rate particularly with individual features .i.e. some weights in the dataset may have separate learning rates than others.