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Novosadov [1.4K]
3 years ago
10

Importance of project risk management

Computers and Technology
1 answer:
kkurt [141]3 years ago
5 0
You must spread your risks out like don’t put all your eggs in one basket... don’t rely on on thing because is it goes downhill you have no backup
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Explain the correct ways of using keyboard. .​
Naily [24]

Answer:

https://www.digitalunite.com/technology-guides/computer-basics/using-computer/how-use-computer-keyboard

Explanation:

the link at the top should explain it all! :)

7 0
2 years ago
Importance of taking correct body measurement....
drek231 [11]

Taking correct body measurements is of key importance especially to models and future models. So that they remember about the leftover weight in their body from thanksgiving , now when they take correct body measurements , they are aware and will counter such things to be better.

6 0
3 years ago
Do you think EA sports should add more tape jobs to the game? (Game is nhl22)
wariber [46]

Answer:

yes

Explanation:

7 0
2 years ago
Now that the classList Array has been implemented, we need to create methods to access the list items.
natima [27]

The following code will be applied to Create the following static methods for the Student class

<u>Explanation:</u>

/* Note: Array index starts from 0 in java so ,, user ask for 1 index then the position will be i-1 , below program is based on this concept if you dont want this way just remove -1 from classList.get(i-1).getName(); ,and classList.add(i-1,student); */

/* ClassListTester.java */

public class ClassListTester

{

public static void main(String[] args)

{

//You don't need to change anything here, but feel free to add more Students!

Student alan = new Student("Alan", 11);

Student kevin = new Student("Kevin", 10);

Student annie = new Student("Annie", 12);

System.out.println(Student.printClassList());

System.out.println(Student.getLastStudent());

System.out.println(Student.getStudent(1));

Student.addStudent(2, new Student("Trevor", 12));

System.out.println(Student.printClassList());

System.out.println(Student.getClassSize());

}

}

/* Student.java */

import java.util.ArrayList;

public class Student

{

private String name;

private int grade;

//Implement classList here:

private static ArrayList<Student> classList = new ArrayList<Student>();

public Student(String name, int grade)

{

this.name = name;

this.grade = grade;

classList.add(this);

}

public String getName()

{

return this.name;

}

//Add the static methods here:

public static String printClassList()

{

String names = "";

for(Student name: classList)

{

names+= name.getName() + "\n";

}

return "Student Class List:\n" + names;

}  

public static String getLastStudent() {

// index run from 0 so last student will be size -1

return classList.get(classList.size()-1).getName();

}

public static String getStudent(int i) {

// array starts from 0 so i-1 will be the student

return classList.get(i-1).getName();

}

public static void addStudent(int i, Student student) {

// array starts from 0 so, we add at i-1 position

classList.add(i-1,student);

// remove extra student

classList.remove(classList.size()-1);

}  

public static int getClassSize() {

return classList.size();

}

}

7 0
3 years ago
From a neural network with 11 input variables, one hidden layer with three hidden units, and one output variable, how many param
e-lub [12.9K]

Answer:

40

Explanation:

Given that:

A neural network with 11 input variables possess;

one hidden layer with three hidden units; &

one output variable

For every input, a variable must go to every node.

Thus, we can calculate the weights of weight with respect to connections to input and hidden layer by using the formula:

= ( inputs + bias) × numbers of nodes

= (11 + 1 ) × 3

= 12 × 3

= 36 weights

Also, For one hidden layer (with 3 nodes) and one output

The entry result for every hidden node will go directly to the output

These results will have weights associated with them before computed in the output node.

Thus; using the formula

= (numbers of nodes + bais) output, we get;

= ( 3+ 1 ) × 1

= 4 weights

weights with respect to input and hidden layer total = 36

weights with respect to hidden and output layer total = 4

Finally, the sum of both weights is = 36 + 4

= 40

6 0
2 years ago
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