Deep Learning - Deep Neural Network for Beginners Using Python - Logistic Regression Algorithm

The transcript discusses an algorithm that starts with random weights and a bias, updating them for each data point until the error is minimized. It draws a comparison with the Perceptron algorithm, hinting at a small but significant difference, which will be discussed after implementing logistic regression.

What are the main components involved in the algorithm discussed in the lecture?

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How do the weights (W) and bias (B) get updated in the algorithm?

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What is the stopping criterion for the algorithm mentioned in the lecture?

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In what way is the discussed algorithm similar to the Perceptron algorithm?

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What hint was provided to understand the difference between the Perceptron algorithm and logistic regression?

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