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Home Forums Assignment NPTEL_ Practical Machine learning with tensor flow Practical Machine Learning with Tensorflow – Assignment 1

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• #403
Abhishek Tyagi
Keymaster

1)- From the below graphs, select the one that satisfies the equation y=σ(0.1∗x) :

2)- Perform z-score normalization and min-max scaling on the given array and select from the given options.

arr = [ 100 , 50 , 400 , 300 , 100 ]

3)- We have a neural network with an input layer of h0 nodes, hidden layers of h1, h2, h3…., h1 nodes respectively and an output layer of h(l+1) nodes. How many parameters does the network have?

\sum \limits_{i=0}^l [(h_i*h_{i+1})+h_{i+1}]

4)- Suppose we need to minimize the following loss function by tuning scalar value w using gradient descent: f(w) = 9 + 4w + w2 . Given w<sub>0</sub> =0, select the best value for learning rate α, such that gradient descent reaches the optimal value in just one step.

5)- In linear regression with MAE loss, if we only had one target Y_train for all the observations X_train, then the optimal value that the prediction Y_pred should have is:

Answer- Median of Y_train

6)- A machine learning model gets an accuracy of 90% on a dataset with 90% positive class and 10% negative class. Can we conclude that the model is a good classifier of the data?

7)- Suppose you are given with the following training data for linear regression

x = [3, 2, 4, 0]

y = [4, 1, 3, 1]

You are using a loss function

J(w, b) = \frac{1}{2} \sum \limits_{i=1}^n [h(x_i) - y_i ] ^2
What is the value of J(1,1)?

8)- In the above question, if (w, b) = (1, 3), what is <i> h(4)</i> ?

9)- If we halve the value of a given feature, what happens to the coefficients of other features estimated by minimizing squared loss function? (assuming no interaction between any two features)

10)- Which of the following model complexity vs. loss function plots is most likely from training data?

• #360350
Anonymous

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