Mindblown: a blog about philosophy.

Q.12 Suppose that you have to train your neural networks over a dataset of 20 GB. You have a RAM of 3 GB. How will you resolve this problem of training large data? Ans.

Ans. There are two ways to do this. The first method is for the versions of Python that are older than 3.5 – Z = np.dot(np.ones((4,3)), np.ones((3,2)))print(Z)array([[3., 3.],[3., 3.],[3., 3.],[3., 3.]]) The second method is for Python version > 3.5, Z = np.ones((4,3)) @ np.ones((3,2))

Ans. print(np.unravel_index(50,(5,6,7)))

Ans. In order to add a border to an array that is filled with 0s, we first make an array Z and initialize it with zeroes. We first import numpy as np. Z = np.ones((5,5)) Then, we perform padding on it with the help of pad() function. Z = np.pad(Z, pad_width=1, mode=’constant’, constant_values=0) print(Z)

Ans. In order to create the identity matrix with numpy, we will use the identity() function. Numpy is imported as np np.identity(3) We will obtain the output as – array([[1., 0., 0.],[0., 1., 0.],[0., 0., 1.]])

Ans. In order to merge the two lists into a single list, we will concatenate the two lists as follows – list1 + list2 We will obtain the output as – [1, 2, 3, 4, 5, 6, 7, 8]

Ans. In order to find the maximum value from each row in a 2D numpy array, we will use the amax() function as follows – np.amax(input, axis=1) Where numpy is imported as np and input is the input array.

Ans. You can create a 1-D array in numpy as follows: x = np.array([1,2,3,4]) Where numpy is imported as np

Ans. Some of the important libraries of Python that are used in Data Science are –

Ans. In order to create the identity matrix with numpy, we will use the identity() function. Numpy is imported as npnp.identity(3) We will obtain the output as – array([[1., 0., 0.],[0., 1., 0.],[0., 0., 1.]])