Deep Learning Optimizer

Deep learning over machine learning is a significant improvement in terms of adaptability, accuracy, and the breadth of potential industrial applications.

Optimizer

An optimizer is a procedure or method that alters neural network properties like weights and learning rates. As a result, it aids in decreasing total loss and raising precision. A deep learning model typically has millions of parameters, making the task of selecting the proper weights for the model challenging. It highlights the importance to select an optimization algorithm that is appropriate for your application. Therefore, before delving deeply into the subject, it is vital to comprehend these algorithms.

You can modify your weights and learning rate using various optimizers.

The optimal optimizer to use, though, depends on the application. One bad idea that crosses a beginner's mind is to try every possibility and pick the one that yields the best results. This might not seem like a big deal at first, but when working with hundreds of gigabytes of data, even one epoch can take a while. You will eventually discover that selecting an algorithm at random is no less than gambling with your valuable time.

Numerous optimizers in Deep Learning.

Gradient Descent

When the gradient for the entire dataset is generated, the weights are updated, which slows down the procedure.

Stochastic Gradient Descent

The model parameters are modified through every iteration of this modified GD methodology.

Stochastic Gradient descent with momentum

Descent in Mini-Batch Gradient

Mini-batch is a new variant of this GD procedure in which small batch sizes of the model parameters are revised.

Adagrad

RMSProp

AdaDelta

Adam

check in details adam optimizer

Here , we covered about optimizers in deep learning and its different types.