Deep Learning - Deep Neural Network for Beginners Using Python - Implementing Basic Perceptron

The video tutorial explains the basic implementation of a perceptron using two features and weights. It starts by introducing variables and assigning values to them. A simple perceptron implementation is demonstrated, followed by a vectorized solution using Numpy for handling multiple features efficiently. The tutorial also covers testing the perceptron and handling errors related to mismatched features and weights.

What are the initial values assigned to X1 and X2 in the perceptron example?

• X1 = 5, X2 = 8

• X1 = 6, X2 = 7

• X1 = 7, X2 = 6

• X1 = 8, X2 = 5

What is the condition used to determine if the perceptron output is accepted?

• Output > 0

• Output < 0

• Output <= 0

• Output >= 0

Which library is used for the vectorized solution in the perceptron implementation?

• Numpy

• TensorFlow

• Scikit-learn

• Pandas

What function is used to perform matrix multiplication in the vectorized perceptron implementation?

• numpy.add

• numpy.matmul

• numpy.multiply

• numpy.dot

What happens if the number of features does not match the number of weights in the perceptron implementation?

• The program ignores extra weights

• The program ignores extra features

• An error is returned

• The program runs with default values

What is the output of the perceptron function when the input features are [6, 7] and weights are [3, 1] with a bias of -25?

• 76

• -2

• 0

• 10

Why is it important for the number of features to match the number of weights in a perceptron?

• To improve computational speed

• To avoid runtime warnings

• To reduce memory usage

• To ensure correct matrix multiplication