Deep Learning by proximity of networking and advanced programming Criteria Points AVOI Part 1 - Question 1 Normalize the train and test data 2 Part 1 - Question 2 Build and train a ANN model as per the above mentioned architecture 10 Part 1 - Question 3 observations on the below plot 2 Part 1 - Question 4 Build and train the new ANN model as per the above mentioned architecture 10 Part 1 - Question 5 observations on the below plot 2 Part 1 - Question 6 Print the classification report and the confusion matrix for the test predictions. observations on the final results 4 Part 2 - Question 1 Complete the below code to visualize the first 10 images from the training data 1 Part 2 - Question 2 One-hot encode the labels in the target variable y_train and y_test 2 Part 2 - Question 3 Build and train a CNN model as per the above mentioned architecture 10 Part 2 - Question 4 observations on the below plot 2 Part 2 - Question 5 Build and train the second CNN model as per the above mentioned architecture 10 Part 2 - Question 6 observations on the below plot 2 Part 2 - Question 7 Make predictions on the test data using the second model 1 Part 2 - Question 8 ur final observations on the performance of the model on the test dataDeep Learning Criteria Points Part 1 - Question 1 Normalize the train and test data 2 Part 1 - Question 2 Build and train a ANN model as per the above mentioned architecture 10 Part 1 - Question 3 your observations on the below plot 2 Part 1 - Question 4 Build and train the new ANN model as per the above mentioned architecture 10 Part 1 - Question 5 observations on the below plot 2 Part 1 - Question 6 Print the classification report and the confusion matrix for the test predictions. observations on the final results 4 Part 2 - Question 1 Complete the below code to visualize the first 10 images from the training data 1 Part 2 - Question 2 One-hot encode the labels in the target variable y_train and y_test 2 Part 2 - Question 3 Build and train a CNN model as per the above mentioned architecture 10 Part 2 - Question 4 observations on the below plot 2 Part 2 - Question 5 Build and train the second CNN model as per the above mentioned architecture 10 Part 2 - Question 6 observations on the below plot 2 Part 2 - Question 7 Make predictions on the test data using the second model 1 Part 2 - Question 8 final observations on the performance of the model on the test dataDeep Learning Criteria Points Part 1 - Question 1 Normalize the train and test data 2 Part 1 - Question 2 Build and train a ANN model as per the above mentioned architecture 10 Part 1 - Question 3 bservations on the below plot 2 Part 1 - Question 4 Build and train the new ANN model as per the above mentioned architecture 10 Part 1 - Question 5 servations on the below plot 2 Part 1 - Question 6 Print the classification report and the confusion matrix for the test predictions. observations on the final results 4 Part 2 - Question 1 Complete the below code to visualize the first 10 images from the training data 1 Part 2 - Question 2 One-hot encode the labels in the target variable y_train and y_test 2 Part 2 - Question 3 Build and train a CNN model as per the above mentioned architecture 10 Part 2 - Question 4 observations on the below plot 2 Part 2 - Question 5 Build and train the second CNN model as per the above mentioned architecture 10 Part 2 - Question 6 vations on the below plot 2 Part 2 - Question 7 Make predictions on the test data using the second model 1 Part 2 - Question 8 observations on the performance of the model on the test dataDeep Learning Criteria Points Part 1 - Question 1 Normalize the train and test data 2 Part 1 - Question 2 Build and train a ANN model as per the above mentioned architecture 10 Part 1 - Question 3 r observations on the below plot 2 Part 1 - Question 4 Build and train the new ANN model as per the above mentioned architecture 10 Part 1 - Question 5 rvations on the below plot 2 Part 1 - Question 6 Print the classification report and the confusion matrix for the test predictions. observations on the final results 4 Part 2 - Question 1 Complete the below code to visualize the first 10 images from the training data 1 Part 2 - Question 2 One-hot encode the labels in the target variable y_train and y_test 2 Part 2 - Question 3 Build and train a CNN model as per the above mentioned architecture 10 Part 2 - Question 4 r observations on the below plot 2 Part 2 - Question 5 Build and train the second CNN model as per the above mentioned architecture 10 Part 2 - Question 6 r observations on the below plot 2 Part 2 - Question 7 Make predictions on the test data using the second model 1.