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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?

Short and Crisp Answer

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.


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.

The learning rate is a crucial hyperparameter in Gradient Descent. If the learning rate is too small, the convergence process will be slow. On the other hand, if it is too large, the algorithm may overshoot the minimum, leading to oscillations or even divergence.There are variants of Gradient Descent, such as Batch Gradient Descent, Mini-batch Gradient Descent, and Stochastic Gradient Descent (SGD), which differ in the number of data points used to compute the gradient at each iteration. Batch Gradient Descent uses the entire training dataset, while SGD uses a single random data point at each iteration. Mini-batch Gradient Descent finds a compromise by using a small subset (mini-batch) of the training data at each step. This allows for a balance between computational efficiency and stability during the training process.The choice of the Gradient Descent variant depends on the size of the dataset and the available computational resources. Stochastic Gradient Descent is widely used in deep learning due to its efficiency and ability to handle large datasets.

3. What are the different variations of Gradient Descent?

Ans i) Stochastic gradient descent (SGD):Instead of computing the gradient using the entire training dataset, SGD randomly selects one training sample at a time to calculate the gradient and update the parameters. This makes it computationally more efficient and allows it to converge faster. However, due to the high variance in each update, it can be noisy and may oscillate around the optimal solution.
  ii) Mini-batch Gradient Descent: This approach is a compromise between Gradient Descent and SGD. It computes the gradient and updates the parameters using a small subset (mini-batch) of the training data, striking a balance between the computational efficiency of SGD and the stability of Gradient Descent. This method is widely used in practice and is often the preferred choice for training large-scale models.
  iii) Batch Gradient Descent: In contrast to SGD and Mini-batch Gradient Descent, Batch Gradient Descent computes the gradient using the entire training dataset before updating the parameters. While this approach is more stable and may reach a more accurate minimum, it can be computationally expensive, especially for large datasets.

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