Summary

DL algorithms have seen a major upgrade recently with the development of generative models such as VAEs and GANs, which contributed significantly to the creation of synthetic datasets. With this development, the fields of CV, NLP, and genomics have profited immensely. In the last chapter on Unsupervised learning using Autoencoders, you were introduced to VAE and in this chapter, you were introduced to GANs and how they can be used to address some of the limitations of genomics data and, improve DL models. First, we looked at the differences between discriminative and generative models, and then next we understood the key components of GANS which are the generator and discriminator, how they are trained and constantly pit against each other in an adversarial way to generate synthetic data as close as possible to real-world data.

Because of GANs ability to generate synthetic data and DL’s requirement for a large amount of data, we see how GANs are used for improving...