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This makes them highly effective for a wide range of computer vision tasks. • Gated Recurrent Unit (GRU): GRUs are a simplified model of LSTMs that mix the input and forget gates into a single "replace gate," lowering the computational complexity whereas still achieving related performance in lots of tasks. At each time step, the RNN takes an enter vector and combines it with the hidden state from the earlier time step to provide an output and replace the current hidden state. The variety of neurons in the output layer depends upon the kind of problem you're making an attempt to unravel. • Output Layer: This layer produces the final output of the network. The generator network generates pretend data samples by learning to map random noise to reasonable-looking information factors. During training, the generator goals to produce data that is indistinguishable from real information, while the discriminator goals to appropriately classify actual and pretend samples. Th is conte nt h as been done　wi᠎th the he lp of GSA C᠎ontent Ge​nera​tor  DEMO᠎