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truth-table

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Developed a truth tables project in C that generates truth tables for logical expressions. Implemented algorithms to evaluate logical expressions and generate truth values for all possible combinations of inputs, providing a tool for analyzing the logical behavior of expressions and verifying their validity.

  • Updated Jul 18, 2022
  • C

Re-adapted implementation of Adversarial neural network model described in Learning to Protect Communications with Adversarial Neural Cryptography (Martín Abadi & David G. Andersen, 2016). The primary code is authored by Liam Schoneveld (https://nlml.github.io/neural-networks/adversarial-neural-cryptography/).

  • Updated May 15, 2017
  • Jupyter Notebook

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