tworks either with gradient descent or metaheuristic, using automatic hyper parameters tuning and custom cost function. A mix inspired by the common tricks on Deep Learning and Particle Swarm Optimization.</p>
<p>(Key words: autoML, Deep Learning, Particle Swarm Optimization, learning rate, minibatch, batch normalization, lambda, RMSprop, momentum, adam optimization, learning rate decay, inverted dropout, particles number, kappa, regression, logistic regression)</p>

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