Write a Python program for applying CNN with considering the following requirements: Shuffle the cifar 10 training set x_train( each input with corresponding label in y_train ) Select 2500 images and you must ensure that each class must contain at least 180 samples Apply CNN that keeps noisy examples. Overall, the pseudocode of CNN is as follows: The code must contain at least one lambda expression The code must contain at one comprehension list It is Not allowed to use the numpy library. Euclidean distance is the distance between two samples in Euclidean space. The formula can be expressed as

Python

Write a Python program for applying CNN with considering the following requirements: Shuffle the cifar 10 training set x_train( each input with corresponding label in y_train ) Select 2500 images and you must ensure that each class must contain at least 180 samples Apply CNN that keeps noisy examples. Overall, the pseudocode of CNN is as follows: The code must contain at least one lambda expression The code must contain at one comprehension list It is Not allowed to use the numpy library. Euclidean distance is the distance between two samples in Euclidean space. The formula can be expressed as: USE THIS CODE  import tensorflowimport cv2from tensorflow import kerasfrom PIL import Imageimport numpy as p (x_train, y_train), (_, _) = tf.keras.datasets.cifar10.load_data() x_train = [cv2.cvtColor(image, cv2.COLOR_BGR2GRAY).flatten().tolist() for image in x_train]##x_train=np.asarray(x_train)print(len(x_train))print(len(x_train[0])) # 32*32 NOTE: PLEAS USE THE CODE I ATTACHED  NOTE 2:…

Artificial Intelligence (Part - 1) ====================  The Towers of Hanoi is a famous problem for studying recursion in computer science and searching in artificial intelligence. We start with N discs of varying sizes on a peg (stacked in order according to size), and two empty pegs. We are allowed to move a disc from one peg to another, but we are never allowed to move a larger disc on top of a smaller disc. The goal is to move all the discs to the rightmost peg (see figure). To solve the problem by using search methods, we need first formulate the problem. Supposing there are K pegs and N disk.     (1) Propose a state representation for the problem?

In Python the only import that may be used is Numpy Implement a function called page_rank which will take as input a numpy array M, which will represent the transition matrix of a directed graph, and a positive integer n. The output will be a numpy array which gives the page ranks of each vertex in the graph represented by M. You will iterate the update process n times.

In C++ language. Sample run should be exact as the one on image. Thanks