APPLICATION OF SUPERVISED LEARNING
NEURAL NETWORK
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
To predict a test data with a perceptron in python we use
|
|
perceptron.fit(X ____ train)
|
|
|
perceptron.fit(X ____ train, y ____ train)
|
|
|
perceptron.predict(X ____ test)
|
|
|
perceptron.predict(X ____ test, y ____ test)
|
Explanation:
Detailed explanation-1: -XOR-ALL (perceptrons) FOR ONE (logical function) They are called fundamental because any logical function, no matter how complex, can be obtained by a combination of those three. We can infer that, if we appropriately connect the three perceptrons we just built, we can implement any logical function!
Detailed explanation-2: -def perceptron(X, y, lr, epochs): # X–> Inputs. m, n = X.shape. # Initializing parapeters(theta) to zeros. theta = np.zeros((n+1, 1)) n miss list = [] for epoch in range(epochs): n miss = 0. for idx, x i in enumerate(X): x i = np.insert(x i, 0, 1).reshape(-1, 1) More items •16-Apr-2021
There is 1 question to complete.