Introduction to Generative Adversarial Networks using Pytorch: BollyGAN

"Generative Adversarial Networks is the most interesting idea in the last 10 years in Machine Learning." Ever heard of "Fake it till you make it!!!"? Generative Deep Learning models do exactly that but with the help of mathematics, statistics and data. It works by learning the latent variables which are the variables responsible for the generation of the input data and then using this latent space to generate synthetic data. Basically, Generative modeling is an unsupervised learning task in Deep learning that involves for a given input, model learning the probability distribution of the variables making up this input and generate something new -- either resembling the input or a new synthetic output. Generative Deep Learning models have been employed in a wide range of applications ranging from language synthesis with LSTM to Google's DeepDream algorithm to executing neural style transfers to generating DeepFakes and new dataset curation.