Deep Learning - Artificial Neural Networks with Tensorflow - Binary Cross Entropy

The lecture explains the cross entropy loss function used in binary classification, emphasizing its basis in probability. It discusses the Bernoulli distribution for binary events, using a coin toss as an example to illustrate maximum likelihood estimation. The lecture details the process of calculating likelihood and log likelihood, highlighting the similarities between binary cross entropy and negative log likelihood. It concludes by comparing the binary cross entropy with mean squared error, noting that both are derived from probability distributions and involve maximum likelihood solutions.

What is the significance of the log likelihood in the context of binary cross entropy?

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How does the binary cross entropy relate to the Bernoulli distribution?

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What conclusion can be drawn about the relationship between binary cross entropy and maximum likelihood estimation?

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