Learning Technology Interview Questions

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Round 2) Some questions that I remember: Explain the programming assignments, why are the weights randomly initialized, what is an activation function, what is the difference between SoftMax and ReLU activation, what are hyperparameters, list down all the hyperparameters, what are color channels, why do we convert images to grayscale, how does. Otsu's image segmentation work, what is the size of the image before and after converting to grayscale.
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Machine Learning Intern

Interviewed at Neva Ventures

4.4
Aug 24, 2018

Round 2) Some questions that I remember: Explain the programming assignments, why are the weights randomly initialized, what is an activation function, what is the difference between SoftMax and ReLU activation, what are hyperparameters, list down all the hyperparameters, what are color channels, why do we convert images to grayscale, how does. Otsu's image segmentation work, what is the size of the image before and after converting to grayscale.

Round 3) Some questions that I remember: Explain academic projects, how does an Artificial Neural Network work, what is feed-forward and backward propagation, what is gradient descent, what is the difference between global minimum and local minima, how do you avoid local minima, what is a parameter, difference between parameter and hype parameter, list down each parameter and hyperparameter, mathematical questions on loss function, what is overfitting, how do you avoid overfitting, what is regularization, where do you add regularization term, questions on image classification using CNN, the question to find the second largest element of an array, and a mathematical puzzle.
avatar

Machine Learning Intern

Interviewed at Neva Ventures

4.4
Aug 24, 2018

Round 3) Some questions that I remember: Explain academic projects, how does an Artificial Neural Network work, what is feed-forward and backward propagation, what is gradient descent, what is the difference between global minimum and local minima, how do you avoid local minima, what is a parameter, difference between parameter and hype parameter, list down each parameter and hyperparameter, mathematical questions on loss function, what is overfitting, how do you avoid overfitting, what is regularization, where do you add regularization term, questions on image classification using CNN, the question to find the second largest element of an array, and a mathematical puzzle.

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