MLT week 12 - real exam questions

You build a fully-connected feed-forward neural network. The input is a -dimensional vector; the network has two hidden layers of and neurons respectively, followed by an output layer of neurons. Every layer is densely connected to the next. , compute the total number of weight parameters in the network. (Enter if it cannot be determined.)

Consider the effect of each operation below on a model's bias and variance. Which of the following statements are correct?

You build a fully-connected feed-forward neural network for a classification task. The input is a - dimensional vector; the network has two hidden layers of neurons each, followed by an output layer of neurons. Every layer is densely (fully) connected to the next. Ignoring all bias terms, compute the total number of weight parameters in the network. (Enter if it cannot be determined.)

Consider a regression problem in which we use a neural network with the following architecture: Input layer has neurons• Two hidden layers, each with neurons• Output layer with one neuron• Find the total number of parameters in the network. Ignore the biases.

Consider a regression problem in which we use a neural network with the following architecture: • Input layer has neurons • Three hidden layers, each with neurons • Output layer with one neuron Find the total number of parameters in the network. Ignore the biases.

What is the most appropriate choice of activation function for the output layer if the binary cross-entropy loss is used?

Consider a simple neural network with one hidden layer. The network has the following architecture: Input layer with 3 neurons. Hidden layer with 2 neurons, using the sigmoid activation function. Output layer with 1 neuron, using the linear activation function. The weights and biases for the network are as follows: Hidden Layer: Neuron 1: Weights: [0.5, -0.2, 0.8] Bias: 0.1 Neuron 2: Weights: [0.4, 0, 0.2] Bias: -0.4 Output Layer: Neuron 1: Weights: [0.2, 0.4] Bias: -0.3 Assume that the input values are [0.6, 0.3, 0.8]. Calculate output of Neuron 1 in hidden layer

Choose all the correct statements about neural networks

What will be the result of applying ReLU to the following values?: -2.7, 3.9, -1.0, 4.2, 6.4, -7.3

Consider a neural network with 8 inputs and 2 outputs. If there are 4 hidden layers each with 4 neurons, how many parameters need to be learnt if there is a bias associated with each neuron in the hidden and output layers?

What will be the result of applying ReLU to the following values?: -3.7, 4.9, -2.0, 5.2, 7.4, -8.3

Consider a neural network with 10 inputs and 2 outputs. If there are 5 hidden layers each with 5 neurons, how many parameters need to be learnt if there is a bias associated with each neuron in the hidden and output layers?

The following is the activation vector output by some hidden layer in a neural network when some input vector is given to it: The following is the activation vector output by some hidden layer in a neural network when some input vector is given to it: Which of the following could be the activation function used in this layer?

Consider a neural network for a multi-class, image classification problem. When the network is trained on the images as they are, it does a good job on the test data. Call the dataset (train + test) for this setup D1 and the network N1. Assume that we now turn all images upside down, in both the training and test dataset. Now, the network with the same architecture is trained from scratch on this modified dataset. Call the dataset (train + test) for this setup D2 and network N2. Select the most appropriate option.

Which of the following could be the vector of probabilities output by a softmax regression model for 5 classes? Note that the options are independent of each other.