A Guide to SQL
A Guide to SQL
9th Edition
ISBN: 9781111527273
Author: Philip J. Pratt
Publisher: Course Technology Ptr
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Chapter 4, Problem 14CAT
Program Plan Intro

“SELECT” command:

The “SELECT” command is used to retrieve data in a database.

Syntax for selecting values from the table is as follows:

SELECT STUDENT_ID FROM STUDENT;

  • The given query is used to display each student ID from “STUDENT” table.

“AVG” function:

  • It is the one function of aggregate function.
  • The “AVG” function is used to compute the average value in a column.

Example:

The example for “AVG” function is given below:

SELECT AVG(MARK_CREDIT) FROM STUDENT;

The above query is used to display the average of mark credit from “STUDENT” table using “AVG” function.

“GROUP BY” Clause:

  • User can group the data using “GROUP BY” clause.
  • This clause allows the user to group data on a specific column and then computes statistics when user preferred.

Example:

The example for “GROUP BY” clause is given below:

SELECT CUSTOMER_NAME, SUM(AMOUNT) FROM CUSTOMERS GROUP BY CUSTOMER_NAME;

The above query is used to list the customer name and the sum of amount using “GROUP BY” clause.

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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
Please provide me with the output  image of both of them . below are the diagrams code I have two diagram : first diagram code  graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…

Chapter 4 Solutions

A Guide to SQL

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