introduction to pytorch learning problems while focusing on the tasks that matter and be able to easily debugexperimentand deploy for the aforementioned reasonspytorch has seen wider adoption in enterprises if you follow the media around deep learningyou might have read articles that mention new large organization adopting pytorch yann lecuna profound researcher in deep learningprofessor at nyuand chief scientist at facebook (at the time this writingtweeted the following in nov "over of neurips' papers that mention using deep learning framework mention pytorch pytorch is dominant in deep learning research (ml/cv/nlp conferencesby wide margin with enough reasons to justify pytorch as worthy choice for deep learninglet' get started it all starts with tensor in generala task in deep learning would revolve around processing an imagetextor tabular data (cross-sectional as well as time-seriesto generate an outcome that is numberlabelmore textanother imageor combination of these simple examples include classifying an image as dog or catpredicting the next word in sentencegenerating captions for an imageor transforming an image with new style (saythe prisma app on ios/androideach of these tasks would need the underlying data to be stored in specific structure processing and developing these solutions will have several intermediate stageswhich will also need specific structure (for examplethe weights of neural networka common structure that could be universally used for storingrepresentingand transforming is tensor