Answer: >>> for count in range(10, 14):
print(count)
Explanation:
just took the test on edg
The United Nations currently recognizes 180 currencies that are used in 195 countries across the world the United States dollar is a popular currency and about 66 countries either peg their currency to the US dollar or use it as their currency
Answer:
The method definition to this question can be given as:
Method definition:
double max(double x, double y) //define method with double parameter
{
if (x>=y) //check condition.
return x; //return value
else
return y; //return value
}
double max(int x, int y) //define method with integer parameter
{
if (x>=y) //check condition
return x; //return value
else
return y; //return value
}
double max(char x, char y) //define method with char parameter
{
if (x>=y) //check condition
return x; //return value
else
return y; //return value
}
Explanation:
The above method definition can be described as below:
- In the first method definition first, we define a method that is "max()". In this method we pass two variables as a parameter that is "x and y" and the datatype of this is double. Then we use a conditional statement. In the if block we check if variable x is greater then equal to y then it will return x else it will return y.
- In the second method definition, we define a method that is same as the first method name but in this method, we pass two integer variable that is "x and y". Then we use a conditional statement. In the if block we check if variable x is greater then equal to y then it will return x else it will return y.
- In the third method definition, we define a method that is same as the first and second method name but in this method, we pass two char variable that is "x and y". Then we use a conditional statement. In the if block we check if variable x is greater then equal to y then it will return x else it will return y.
Answer:
A neuromorphic computer is a machine comprising many simple processors / memory structures (e.g. neurons and synapses) communicating using simple messages (e.g. spikes). ... Neuromorphic computing systems excel at computing complex dynamics using a small set of computational primitives (neurons, synapses, spikes).
Explanation:
The structure of neuromorphic computers makes them much more efficient at training and running neural networks. They can run AI models at a faster speed than equivalent CPUs and GPUs while consuming less power. This is important since power consumption is already one of AI's essential challenges.
