Q1. Which of the following is an example of unstructured data?
Audio
Images
Text
All of the above
Answer: 4 Explanation: All of these are examples of unstructured data. Refer to this link to know more.
Q2. Which of the following is a model-centric AI development?
Hold the data fixed and iteratively improve the code/model
Hold the code/model fixed and iteratively improve the data
Answer: 1 Explanation: As clear from the name, in model-centric AI development, we hold the data fixed and iteratively improve the code/model
Q3. What is Semi-Supervised Learning?
where for each example we have the correct answer/label and we infer a mapping function from these examples
where for each example we don’t have the correct answer/label and we try to find some sort of structure or pattern in the dataset
where for some examples we have the correct answer/label while for others we don’t have correct answer/label
Answer: 3 Explanation: As clear from the name, in Semi-Supervised learning for some examples we have the correct answer/label while for others we don’t have correct answer/label. Because nowadays we are able to collect huge amount of data and labelling this huge data takes enormous effort so the focus is now shifting to Semi-Supervised learning. This is also known as Self-Supervised learning. Why? Because sometimes the data can be unlabelled but the data itself provides the necessary context which would make up the labels. For instance, CBOW model for creating word embeddings.
Q4. Which of the following is the reason to use non-linear activation function on neural networks?
If you use only linear activation function, then no matter how many layers you use it will be same as not using any hidden layers
Hidden layer with linear activation functions is of no use as it is not adding any non-linearity to the network so the network will not be able to learn complex functions
Adding n number of hidden layers with linear activation function, end up summing it to another linear function
All of the above
Answer: 4 Explanation: All of the above are possible reasons. Refer to this beautiful explanation by Andrew Ng to know more.
Q5. Which of the following activation functions can be used in neural network?
ReLU
Tanh
Sigmoid
All of the above
Answer: 4 Explanation: All of the above activation functions can be used in neural networks. Refer to this beautiful explanation by Andrew Ng to know more.
Q6. RMSprop resolves the limitation of AdaGrad optimizer?
True
False
Answer: 1 Explanation: RMSprop divides the learning rate by exponentially decaying average of squared gradients whereas AdaGrad divides the learning rate by sum of squared gradients. This in turn causes the learning rate to shrink in AdaGrad and eventually become infinitesimally small, at which point the algorithm is no longer able to acquire additional knowledge. Refer to this link to know more.
Q7. If you increase the value of lambda (regularization parameter), then model will always perform better as it helps in reducing the overfitting of model.
True
False
Answer: 2 Explanation: As we increase the regularization hyperparameter lambda, the weights starts becoming smaller. This can also be verified by the weights update equation in gradient descent (with L2 regularization) which is w=w(1-α*λ/m)-α*dLoss/dw. So, as you increase λ to a very high value, weights become closer to 0. This leads to a model that is too simple and ends up underfitting the data thus decreasing the performance of the model. Refer to this beautiful explanation by Andrew Ng to know more.
Q8. What is a multi-task learning in deep learning?
Train n different neural networks to learn n tasks
Train a single neural network to learn n task simultaneously
Answer: 2 Explanation: In multi-task learning, we train a single neural network to learn n task simultaneously. For instance, self driving cars has to detect pedestrains, cars, traffic lights etc.