APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
Only weight
|
|
|
Only Bias
|
|
|
Both weight and bias
|
|
|
Learning rate
|
Detailed explanation-1: -The gradient descent algorithm seeks to change the weights so that the next evaluation reduces the error, meaning the optimization algorithm is navigating down the gradient (or slope) of error.
Detailed explanation-2: -Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples. In supervised learning, a machine learning algorithm builds a model by examining many examples and attempting to find a model that minimizes loss; this process is called empirical risk minimization.
Detailed explanation-3: -Gradient descent is a method for finding the minimum of a function of multiple variables. So we can use gradient descent as a tool to minimize our cost function.