ML practitioners wrangle data to create the best training datasets possible for their models. This generally means that the data is unbiased, well-structured, and will help to train a model that gives good predictive performance in the real world. Whether you collect data yourself from the real world, use an
Machine learning practitioners are increasingly turning to the power of generative adversarial networks (GANs) for image processing. Applications that really benefit from using GANs include: generating art and photos from text-based descriptions, upscaling images, transferring images across domains (e.g., changing day time scenes to night time), and many others.