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Deep-ML Login Sign Up Back to Problems Your Solution Full Screen Single Neuron with 1 import numpy as np Backpropagation (medium) 3 def train_neuron(features: np.ndarray, labels: n 4 # Your code here Write a Python function that simulates a single neuron with sigmoid 5 return updated_weights, updated_bias, mse_va activation, and implements backpropagation to update the neuron's weights and bias. The function should take a list of feature vectors, associated true binary labels, initial weights, initial bias, a learning rate, and the number of epochs. The function should update the weights and bias using gradient descent based on the MSE loss, and return the updated weights, bias, and a list of MSE values for each epoch, each rounded to four decimal places. Example Submit Code Example: input: features = [[1.0, 2.0], [2.0, 1.0], [–1.0, output: updated_weights = [0.0808, -0.1916], upda Output will be shown here.
