Batch gradient descent is a type of gradient descent that update the parameters after forward and backward pass through the entire dataset. It is called "batch" gradient descent because it uses the entire dataset to compute the gradient of the loss function at each iteration. Where n is the number of samples in the entire dataset. One of the main disadvantages of batch gradient descent is that it can be computationally expensive when the dataset is very large, as it requires a forward and backward pass through the entire dataset at each iteration. In addition, if the dataset is noisy or has a lot of outliers, the loss function can oscillate and never converge to a minimum. In this case, a more sophisticated optimization algorithm such as stochastic gradient descent or mini-batch gradient descent may be more appropriate.

This article was written by Roopam Upadhyay. Roopam is a seasoned professional of advanced analytics with more than a decade of experience in statistical modeling, data science, predictive analytics, optimization, & business consulting. They learn the same way as humans. Humans learn from experience and so do machines. For machines, experience is in the form of data.

This article was written by Roopam Upadhyay. Roopam is a seasoned professional of advanced analytics with more than a decade of experience in statistical modeling, data science, predictive analytics, optimization, & business consulting. They learn the same way as humans. Humans learn from experience and so do machines. For machines, experience is in the form of data.