# Machine Learning interview on optimizer (Gradient Descent)

Hi Everyone I am going to start postng Intervew questions topicwise. This is part one of the posts where I’ll list down the interview questions based on gradient descent and don’t forget to share it as much as you can to make it accessible to everyone and let others know about it.

#### 1. What is an optimizer and what is its purpose in machine learning?

Short and Crisp

An optimizer in machine learning is an algorithm used to adjust the parameters of a model during the training process. The purpose of an optimizer is to minimize a loss function, which measures the error between the model’s predictions and the actual target values.

When training a machine learning model, the goal is to find the optimal set of parameters that minimizes a predefined loss or cost function. The loss function quantifies how well the model’s predictions match the actual target values in the training data. By minimizing the loss function, the model improves its ability to make accurate predictions on new data.

Optimizers play a crucial role in the training process of machine learning models, especially in deep learning, where models can have millions of parameters. The training process typically involves iteratively updating the model’s parameters based on the gradients of the loss function with respect to those parameters. The optimizer uses these gradients to determine the direction and magnitude of the updates to the model’s parameters in each training step.

#### 2. What is Gradient Descent and how does it work?

Gradient Descent is an optimization algorithm used to minimize a loss function, by finding the optimal values of the model’s parameters. In the context of machine learning, GD is used to update the parameters of a model during the training process, with the goal of reducing the error or loss between the model’s predictions and the actual target values.

Explanation:

The primary objective of Gradient Descent is to iteratively update the parameters of a model to minimize a given loss function. In the context of machine learning, the model’s parameters represent the weights and biases of the model, and the loss function measures how well the model’s predictions match the actual target values on the training data.Here’s how Gradient Descent works:

1. Initialization: The process begins by initializing the model’s parameters (weights and biases) with random values.
2. Compute the Loss: The loss function is evaluated using the current values of the model’s parameters on a batch of training data. The loss function quantifies how well the model is performing; it is usually a differentiable function.
3. Calculate the Gradient: The gradient of the loss function with respect to each model parameter is computed. The gradient essentially points in the direction of the steepest increase of the function. It tells us how much the loss function will change if the corresponding parameter is adjusted.
4. Update Parameters: The model’s parameters are updated in the opposite direction of the gradient to minimize the loss function. This update is performed according to the learning rate (a hyperparameter), which determines the step size taken during each iteration of Gradient Descent.

Mathematically, the parameter update in Gradient Descent can be expressed as follows, assuming we have parameters θ and the loss function L:θ_new = θ – learning_rate * gradient(Loss with respect to θ)

1. Repeat: Steps 2-4 are repeated for a fixed number of iterations or until the loss converges to a desired value.