Machine Learning, Deep Learning, Neural Networks, Data Science, Towards Data Science.
takes a random input z with density p_z and returns an output x_g = G(z) that should follow (after training) the targeted probability distribution a discriminative network D(.) that takes an input x that can be a “true” one (x_t, whose density is denoted p_t) or a “generated” one (x_g, whose