MCQ IN COMPUTER SCIENCE & ENGINEERING

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COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
Which of the following are required for a machine learning model to compile properly? Select all that apply.
A
Training examples
B
A loss function
C
An optimizer function
D
Test examples, a loss function, and an optimizer function
Explanation: 

Detailed explanation-1: -Dataset. A dataset is the starting point in your journey of building the machine learning model. Simply put, the dataset is essentially an M×N matrix where M represents the columns (features) and N the rows (samples).

Detailed explanation-2: -In short, machine learning requires statistics, probability, calculus, linear algebra, and knowledge of programming.

Detailed explanation-3: -An optimizer is a function or an algorithm that modifies the attributes of the neural network, such as weights and learning rate. Thus, it helps in reducing the overall loss and improve the accuracy.

Detailed explanation-4: -An optimizer is one of the two arguments required for compiling a Keras model: from tensorflow import keras from tensorflow.keras import layers model = keras. Sequential() model. add(layers. Dense(64, kernel initializer=’uniform’, input shape=(10, ))) model.

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