OptiSol Business Solutions Machine Learning Engineer Interview Questions, Process, and Tips

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Q1. What is dropout?
Ans. 
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 comm...read more
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Q2. What is batch Normalization
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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, ...read more
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Q3. What is Random Forest
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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 ta...read more
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Q4. What is confusion matrix
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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: ...read more
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Q5. What is a recurrent neural network
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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 gra...read more
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