Answer:
Explanation:
One group of students did an experiment to study the movement of ocean water. The steps of the experiment are listed below.
Fill a rectangular baking glass dish with water.
Place a plastic bag with ice in the water near the left edge of the dish.
Place a lighted lamp near the left edge of the dish so that its light falls directly on the plastic bag.
Put a few drops of ink in the water.
The student did not observe any circulation of ink in the water as expected because the experiment had a flaw. Which of these statements best describes the flaw in the experiment? (2 points)
Not enough ink was added.
Not enough water was taken.
The dish was too small for the experiment.
The lamp and the ice bag were at the same place.
Answer:
The answer is "a1 and a2 is an array of pointers".
Explanation:
In this question, A collection of pointers refers to an array of elements where each pointer array element points to a data array element. In the above-given statement, the two-pointer type array "a1 and a2" is declared that holds the same size "8" elements in the array, and each element points towards the array's first element of the array, therefore, both a1 and a2 are pointer arrays.
Answer: hello your question is poorly written and I have been able to properly arrange them with the correct matching
answer
Static libraries : C
Dynamic link libraries: A
Using static libraries: B
Making some changes to DLL: D
Explanation:
Matching each term with its meaning
<u>Static Libraries </u> : Are attached to the application at the compile time using the Linker ( C )
<u>Dynamic link libraries</u> ( DLL ) : Is Loaded at runtime as applications need them ( A )
<u>Using static Libraries </u>: Makes your program files larger compared to using DLL ( B )
<u>Making some changes to DLL </u>: Does not require application using them to recompile ( D )
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
