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.

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