Your question is Neural Network With Sigmoid vs Logistic Regression. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What are the similarities of a neural network of 1 neuron with a sigmoid activation function and a logistic regression?
Explain the mathematical equivalence, including the role of weights, bias, sigmoid activation, logits, and binary cross-entropy loss. Provide a small Python demonstration that trains both models on the same binary classification data and compares their predicted probabilities and decision boundaries. Mention the assumptions and implementation details that can cause their outputs to differ.