TVS Motor Interview Questions and Answers

A recurrent neural network (RNN) is a type of neural network designed to handle sequential data by maintaining a memory of previous inputs.

• RNNs have loops that allow information to persist, making them suitable for tasks like speech recognition, language translation, and time series prediction.

• They can process inputs of variable length and are capable of learning patterns in sequences.

• RNNs suffer from the vanishing gradient problem, which can make it difficult for them to lea...read more

Dropout is a regularization technique used in neural networks to prevent overfitting by randomly setting some neuron outputs to zero during training.

• Dropout is a regularization technique used in neural networks to prevent overfitting.

• During training, a fraction of neurons are randomly selected and their outputs are set to zero.

• This helps in preventing co-adaptation of neurons and improves generalization.

• Dropout is commonly used in deep learning models like CNNs and RNNs.

• Examp...read more

Random Forest is an ensemble learning method that builds multiple decision trees and merges them to improve accuracy and prevent overfitting.

• Random Forest is a collection of decision trees that are trained on random subsets of the data.

• Each tree in the Random Forest independently makes a prediction, and the final prediction is determined by a majority vote.

• Random Forest is effective for classification and regression tasks.

• It helps in reducing overfitting and increasing the ac...read more

Batch Normalization is a technique used to improve the training of deep neural networks by normalizing the input of each layer.

• Batch Normalization helps in reducing internal covariate shift by normalizing the input of each layer.

• It speeds up the training process by allowing higher learning rates and reducing the dependence on initialization.

• It can be applied to convolutional neural networks, recurrent neural networks, and other types of deep learning models.

• Example: In a CNN,...read more

Confusion matrix is a table used to evaluate the performance of a classification model.

• It is a matrix with rows representing the actual class and columns representing the predicted class.

• It helps in understanding the performance of the model by showing true positives, true negatives, false positives, and false negatives.

• It is commonly used in machine learning to evaluate the accuracy of a classification model.

• Example: In a binary classification problem, a confusion matrix wou...read more

We followed various problem-solving methods while using Selenium.

• Identifying the root cause of the issue

• Analyzing the logs and error messages

• Collaborating with developers to resolve issues

• Using debugging tools like Firebug and Chrome Developer Tools

• Implementing Page Object Model design pattern

• Using TestNG framework for test execution and reporting