Deep learning (DL) is hot. And it has cool tools to play with. Although scores of DL practitioners started their journey with TensorFlow, PyTorch has become an equally popular deep learning framework, since it was introduced by Facebook AI Research (FAIR) team, back in early 2017. Since its introduction, it has caught the attention of AI researchers and practitioners around the world and has matured significantly. In essence, PyTorch provides tremendous flexibility to a programmer about how to create, combine, and process tensors as they flow through a network (called computational graph) paired with a relatively high-level, object-oriented API. Raw TensorFlow, of course, provides a similar level of low-level flexibility but it is often difficult to master and troubleshoot.