Deep Learning - Crash Course 2023 - Class Sigmoid

The video tutorial covers the implementation of a sigmoid neuron class, including methods for initialization, prediction, loss calculation, and accuracy assessment. The tutorial demonstrates how to calculate the weighted sum, predict outputs, compute mean squared error loss, and visualize loss reduction. It also explains binary classification by mapping outputs to binary values and calculating accuracy using the SKLN library. The tutorial concludes with running the model and observing improvements in accuracy.

What is the purpose of the 'init' method in the sigmoid neuron class?

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Explain how the weighted sum is calculated in the sigmoid neuron.

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How does the sigmoid function contribute to the prediction output?

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Describe the process of computing the loss in the sigmoid neuron class.

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What is the significance of the threshold value in the binary classification problem?

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How is accuracy calculated in the context of the sigmoid neuron model?

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What improvements can be made to the model's accuracy based on the learning rate and epochs?

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