Answer:
be clearer. your question doesn't seem to be presented well
Answer:
don't have name, but would be something cool.
Explanation:
poor, have a splendid day.
Answer:
import numpy as np#importing numpy module with an alias np.
def c(bitstring_array):# defining function c.
num_integer=bitstring_array.dot(2**np.arange(bitstring_array.size)[::-1])#bitstring conversion.
return num_integer#returning integer array.
print("Enter bits")
Bit_l=input().split(" ")#enter space separated bitstring.
for i in range(len(Bit_l)):#iterating over the bitstring.
Bit_l[i]=int(Bit_l[i])
bitstring_array=np.array(Bit_l)
print(c(bitstring_array))#function call.
Output:
Enter bits
1 1 1 0 0 1
57
Answer:
The answer is "using validation error".
Explanation:
The validation error is used to response the test for one of the queries is activated to the participant, which may not properly answer the question. These errors go up continuously after each time, the processing rate is too high and also the method is different.
- These errors are also unless to increase when they are actually in the problem.
- The training level will be that, if the learning error may not increase when the model overrides the learning set and you should stop practicing.
Answer:
Its word count so she knows how much words she is writing
Explanation:
