10+ Hazra Group Interview Questions and Answers

Object detection using CNN involves training a neural network to identify and locate objects within an image.

• CNNs use convolutional layers to extract features from images

• These features are then passed through fully connected layers to classify and locate objects

• Common architectures for object detection include YOLO, SSD, and Faster R-CNN

Central Limit Theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal.

• The theorem applies to large sample sizes.

• It is a fundamental concept in statistics.

• It is used to estimate population parameters from sample statistics.

• It is important in hypothesis testing and confidence intervals.

• Example: If we take a large number of samples of the same size from a population, the distribution of the sample means will b...read more

Gradient descent is an iterative optimization algorithm used to minimize a cost function by adjusting model parameters.

• Gradient descent is used in machine learning to optimize models.

• It works by iteratively adjusting model parameters to minimize a cost function.

• The algorithm calculates the gradient of the cost function and moves in the direction of steepest descent.

• There are different variants of gradient descent, such as batch, stochastic, and mini-batch.

• Gradient descent can...read more

Image segmentation is the process of dividing an image into multiple segments or regions.

• It is used in computer vision to identify and separate objects or regions of interest in an image.

• It can be done using various techniques such as thresholding, clustering, edge detection, and region growing.

• Applications include object recognition, medical imaging, and autonomous vehicles.

• Examples include separating the foreground and background in an image, identifying tumors in medical i...read more

R-squared measures the proportion of variance explained by the model, while Adjusted R-squared adjusts for the number of predictors in the model.

• R-squared is the proportion of variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, with 1 indicating a perfect fit.

• Adjusted R-squared penalizes the addition of unnecessary predictors to the model, providing a more accurate measure of the model's goodness of fit.

• R-squared can i...read more

Classification metrics like accuracy, precision, and recall are used to evaluate the performance of a classification model.

• Accuracy measures the overall correctness of the model's predictions.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive predictions out of all actual positive instances.

Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.

• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and then combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model corrects the errors made by the previous ones. Examples inc...read more

Various types of joins in SQL include inner join, outer join, left join, right join, and full join.

• Inner join: Returns rows when there is a match in both tables.

• Outer join: Returns all rows when there is a match in one of the tables.

• Left join: Returns all rows from the left table and the matched rows from the right table.

• Right join: Returns all rows from the right table and the matched rows from the left table.

• Full join: Returns rows when there is a match in one of the tables...read more

Feature selection techniques help in selecting the most relevant features for building predictive models.

• Filter methods: Select features based on statistical measures like correlation, chi-squared test, etc.

• Wrapper methods: Use a specific model to evaluate the importance of features by adding or removing them iteratively.

• Embedded methods: Feature selection is integrated into the model training process, like LASSO regression.

• Principal Component Analysis (PCA): Transform the da...read more

Find the greatest number from an array of strings.

• Convert the array of strings to an array of integers.

• Use a sorting algorithm to sort the array in descending order.

• Return the first element of the sorted array as the greatest number.