Gradient Descent in Machine Learning

Optimization in Machine Learning

The process of optimization involves iteratively training the model to get an evaluation of the highest and lowest function. One of the most important warnings in machine learning is to get better results.

We optimize our machine learning models for what reasons? By altering the hyperparameters in each shift until we reach the ideal results, we compare the outcomes in each iteration. We develop a model that is accurate and has a low error rate. There are distinct methods that we can use to optimize a model.

Approaches that fall under the first-order optimization category, such as grade descent and its variations, are frequently encountered.

Gradient Descent

The most widely used optimization method is gradient descent. This method's goal is to iteratively streamline the variables in the opposite direction of the objective function's gradients. This methodology directs the model to detect the target and gradually build up to the ideal value of the objective function with each update.

Stochastic Gradient Descent

To overcome the computing challenge involved in each replication for big scale data, stochastic gradient descent (SGD) was proposed.

In this process, a single sample was used to update the grade (theta) for each repeat rather than directly calculating the grade's precise value. The real grade and the stochastic grade are equally valued. This optimization technique eliminates some computational duplication while decreasing the update time for handling huge numbers of data.

This method's drawback is that it necessitates numerous updates, and gradient descent stages are noisy. As a result, the gradient may change and become extremely computationally expensive.

Adam Optimizer

The Adam Optimizer can deal with the noise issue and operates effectively even with big datasets and parameters.

RMSProp

Because RMSProp balances out the step size, it is feasible to standardize the gradient itself.

Conclusion

In this blog, we covered about optimization in machine learning , Gradient Descent ,Stochastic Gradient Descent .