Based on the above data, answer the given subquestions.
Update the weight values using the perceptron learning algorithm by visiting the following points
in order (x4, x3). What is the angle (in degrees) between the updated weight vector and the initial
weight vector?
A published solution is not available for this question yet.
Look at the contours of a hill shown below. Suppose a person walks from location A to location C
via location B. Then choose the correct statements from the following list of statements
6406531932835.
The person has to do one steep ascending and one steep descending
6406531932836.
The person has to do two steep ascending and two steep descending
6406531932837.
The person has to do one steep ascending and two steep descending
6406531932838.
The person has to do two steep ascending and one steep descending
A published solution is not available for this question yet.
Consider a neural network with three hidden layers and one output layer. The hidden layers
contain 100 neurons each. Suppose we have a square image of size 30×30 containing either a cat
(positive class) or a dog (negative class). The neural network is designed to recognize it by
outputting a probability score for each class. The input image is flattened into an array of size 900.
Assume that all neurons in the network have bias associated with them and use the sigmoid
activation function. How many parameters are there in the network?
A published solution is not available for this question yet.
Suppose that a team solves a 1000-class classification problem using a neural network that
contains 32 layers. Assume that the training set contains exactly 32 samples per class. The team
creates batches of samples, each of size 32. The batch is created such that all the samples in a
batch belongs to the same class.
Based on the above data, answer the given subquestions.
Suppose that the team trains the neural network with the given configuration using mini-batch
Gradient Descent algorithm for 32 epochs. Then how many times do the parameters of the
network get updated?
A published solution is not available for this question yet.
Suppose that a team solves a 1000-class classification problem using a neural network that
contains 32 layers. Assume that the training set contains exactly 32 samples per class. The team
creates batches of samples, each of size 32. The batch is created such that all the samples in a
batch belongs to the same class.
Based on the above data, answer the given subquestions.
Suppose we use SGD (Stochastic Gradient Descent) algorithm to update the parameters of the
network by running it for 32 epochs. Then how many times do the parameters of the network get
updated?
A published solution is not available for this question yet.
