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This project aims to use genetic algorithms to boost the learning of DNN. Building and training a family of NN with same structure and hyperparameters from scratch but starting from different random weights. After a few epochs of training, the networks that perform better are chosen and crossover their weights in order to mating and produce the next generation.
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