Deep Learning - Artificial Neural Networks with Tensorflow - Mean Squared Error

The video tutorial explains the mean squared error from a probabilistic perspective, emphasizing its importance in linear regression. It discusses why errors are squared instead of using absolute values and introduces maximum likelihood estimation using Gaussian distribution. The tutorial also covers the use of calculus to maximize likelihood and solve for parameters, highlighting the relationship between log likelihood and error functions. Finally, it provides a probabilistic interpretation of error functions, preparing viewers to understand cross entropy loss.

What is the primary reason for squaring the error in mean squared error?

• To ensure error values are small

• To reduce computation time

• To simplify calculations

• To make the error always positive

Which method is used to estimate parameters like the mean in a Gaussian distribution?

• Gradient Descent

• Least Squares Estimation

• Maximum Likelihood Estimation

• Bayesian Estimation

Why is the logarithm used when maximizing the likelihood function?

• It increases the likelihood

• It reduces the number of variables

• It simplifies the derivative

• It is a decreasing function

What is the relationship between maximizing likelihood and minimizing mean squared error?

• They are unrelated

• Maximizing likelihood is the same as minimizing mean squared error

• Maximizing likelihood increases mean squared error

• Minimizing mean squared error decreases likelihood

What is the significance of the constants C1 and C2 in the log likelihood expression?

• They are superfluous and do not affect the final result

• They affect the final estimate of the mean

• They determine the shape of the distribution

• They are used to calculate the variance

In the context of linear regression, what assumption is made about the error distribution?

• Errors are uniformly distributed

• Errors follow a Poisson distribution

• Errors are exponentially distributed

• Errors are Gaussian distributed with mean zero

How can the error function in linear regression be interpreted probabilistically?

• As a binomial distribution

• As a Gaussian distribution centered at zero

• As a uniform distribution

• As a Poisson distribution