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To differentiate the formula for a binary classifier in PyTorch, you can use the PyTorch Autograd package which allows automatic computation of gradients.

1. First, define the input data and the corresponding labels.
2. Define the weights and biases for the binary classifier.
3. Compute the predictions by multiplying the input data with the weights, adding the biases, and passing the result through a sigmoid activation function.
4. Define the loss function, such as cross-entropy loss, which calculates the difference between the predicted labels and the ground truth labels.
5. Use the PyTorch Autograd package to compute the gradients of the loss with respect to the weights and biases.
6. Update the weights and biases using the computed gradients and an optimization algorithm such as stochastic gradient descent.
7. Repeat steps 3-6 for multiple training iterations until the loss converges or reaches a desired value.

By differentiating the loss function with respect to the weights and biases using the Autograd package, PyTorch automatically calculates the gradients needed for backpropagation, making it easier to train neural networks.