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An educational module meant to serve as a prelude to talking about automatic differentiation in deep learning frameworks (such as in the Autograd module of PyTorch)

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for all information related to this module, including information related to the latest changes to the code.

from ComputationalGraphPrimer import *

cgp = ComputationalGraphPrimer(
               expressions = ['xx=xa^2',
               output_vars = ['xw'],
               dataset_size = 10000,
               learning_rate = 1e-6,
               grad_delta    = 1e-4,
               display_vals_how_often = 1000,

cgp.gen_gt_dataset(vals_for_learnable_params = {'ab':1.0, 'bc':2.0, 'cd':3.0, 'ac':4.0})

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