Answer:
Please check the explanation.
Explanation:
The US education department mentioned that in 1900 there were only 10% of students enrolled in the schools, however by the end of 1992, the percentage increased by 85% and it became 95%. In 1930 1 million students were enrolled in university. And by 2012, it became 21.6 million. The teachers began to follow a new type of teaching, and the academics began to follow a special method for communique, education as well as for helping the students understand the concepts.
Though, after the 1980s the personal computer came into being in schools and colleges. Since then numerous versions of PC have originated in the marketplace as well as mobiles tracked by Smartphones, and now nearly 100% of people in the US have smartphones. Virtual reality, augmented reality, AI, Machine learning. etc. has cemented the way for the virtual classrooms. Also, each subject is now up with fruits and not just-food. The consequence is such a delightful setup of the Virtual schoolrooms in the entire US, and all over the ecosphere. The projectors, VR devices, AI applications for education, online classroom facility, Electronic version of chalkboards, and in fact everything is no sophisticated, and it is making not only teaching easy but learning as well. And the result is, students are ending up with better results, and teachers seem to be happier and more relaxed. And that is making school management satisfied as well.
5 Common Ethical Issues in the Workplace
Unethical Leadership.
Toxic Workplace Culture.
Discrimination and Harassment.
Unrealistic and Conflicting Goals.
Questionable Use of Company Technology.
Physical Components to a computer are called hardware.
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
Asyncronous is the answer I think