By learning how to follow, one can be a good leader because: Looking up to a leader and following them help to:
- Keep one's ego in check and one can be able to be a good ego manager.
- They create strong credibility.
- They help use to focus our efforts for maximum impact.
<h3>How does being a good follower make you a good leader?</h3>
As a good follower, a person can be able to have the boldness and confidence to be able to respectfully talk about a lot of things with their leader if you see that you're not going in the right way.
Note that one can trust your leader and this will boast up the spirit of your input and engagement in all.
Hence, By learning how to follow, one can be a good leader because: Looking up to a leader and following them help to:
- Keep one's ego in check and one can be able to be a good ego manager.
- They create strong credibility.
- They help use to focus our efforts for maximum impact.
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Answer:
Following is the code in python language
team_names = ('Rockets','Raptors','Warriors','Celtics')#holding the string value
print(team_names[0],team_names[1],team_names[2],team_names[3])#display
Output:
Rockets Raptors Warriors Celtics
Explanation:
Following is the description of above statement .
- Create a dictionary "team_names" that is holding the string value Rockets Raptors Warriors and Celtics.
- Finally we used the print function in that function we pass the index of corresponding dictionary i.e team_names[0] . it will display the first index value similarly we pass team_names[1], team_names[2] team_names[3].
Answer: PCI, The PCI provides the highest performance
Yes actually it is it’s a machine to use for opening bottles such as cans n other
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
