How are the parameters updated during Gradient Descent process?

  1. One at a time
  2. Simultaneously
  3. Sequentially
  4. Not updated

Answer – (B) Simultaneously

“Simultaneously” is the correct choice for the question “How are the parameters updated during Gradient Descent process?”. This is because it is a basic optimization technique that is normally used in machine learning. Also, you can find this in mathematical optimization as well. It can reduce a function by sequencing going in a vertical direction. In terms of machine learning, you will find that this is used everywhere to update the parameters of an AI (Artificial intelligence) model. This method will slash the overall cost or loss function.

Understanding the Gradient Descent Process

The whole Gradient Descent process starts with defining a loss function which will then measure how well your model will perform on a given dataset. The main goal of this process will be to minimize this function by making changes as well as adjusting your model’s parameters. The variables in your models are the parameters that will get tuned in the training process to guesses that fall align closely with what you want in terms of outcome.

The core concept of Gradient Descent has calculations related to the loss function for each and every parameter in that gradient. This gradient will show the rate at which the changes are applied to the function of each parameter. Other than that, it also points out in the direction where the ascent is almost vertical. When you move in the opposite direction, the function value will decrease as well. This is to help your model come in the direction with the most optimal solution.

Mathematically speaking, the rule of update which is used for the parameters in the Gradient Descent can be found by subtracting the product of the gradient as well as the predefined learning rate from the current parameter values. You also need to understand that the learning rate is a hyperparameter. And it is used to control the size of the steps taken during each version.

If you take a bigger learning rate then it might give you a result where there is faster convergence but it could overshoot the minimum. Whereas, with a smaller learning rate, it could only take smaller steps but require more versions to come together and could also get stuck in local minima. There are a number of different Gradient Descent algorithms, where Batch Gradient Descent is used almost everywhere and Stochastic Gradient Descent and Mini-batch Gradient Descent are most used in few places.

Drawbacks of the Gradient Descent Process

When you go through the Gradient Descent Processes you will find that it has a set of drawbacks in addition to all the benefits you get from using this algorithm. One such limitation is getting stuck in saddle points or local minima, particularly in high-dimensional spaces. Nevertheless, you can get rid of this limitation by using different techniques such as momentum, adaptive learning rates (e.g., AdaGrad, RMSprop, Adam), and early stopping.


Gradient Descent holds a lot of importance in machine learning especially when optimizing models. When you get more in-depth into this whole concept you will find that the parameters updated during the Gradient Descent process are done Simultaneously. Read the answer above to learn more about it in detail. 

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