Answer:I love Python, very useful
Explanation:python is very easy and user friendly!
Is simple likeness. Historically, in fact, artists used self-portraits as a kind of calling card, attesting to their ability to capture a likeness and giving a sense of their capabilities. And, yes, self-portraits are convenient exercises because the model is always available and works for free
Answer:
i personally think its a and c i could be wrong tho.
Explanation:
Answer:
Lexical rules that are defined in case of regular grammar are simple and the notation is quite easy to understand.
Regular expression are useful for defining constructs of identifiers or constants. e.g. a|b etc.
In the case of context-free, grammar is not simple and deals with the productions.
Context-free are useful in describing the nested constructs like if-else etc which are not defined by regular expressions.
These produce a higher level of reliability as it provides a medium for generating syntactical as well as semantic data. The grammar is context-free is a little complex.
Explanation:
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
