# Train the GAN for epoch in range(100): for i in range(len(x_train)): # Sample a random noise vector noise = tf.random.normal([1, 100])
You can access a free preview of the first chapter via Manning's AWS S3 bucket to get a feel for the teaching style. Core Topics Covered gans in action pdf github
The key idea behind GANs is to train the generator network to produce synthetic data samples that are indistinguishable from real data samples, while simultaneously training the discriminator network to correctly distinguish between real and synthetic samples. This adversarial process leads to a minimax game between the two networks, where the generator tries to produce more realistic samples and the discriminator tries to correctly classify them. # Train the GAN for epoch in range(100):