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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)

Project description

Consult the module API page at

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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