Python is very well known for its ecosystem of mature scientificcomputing packages. Despite that, the rapidly rising popularity of deeplearning resulted in creation of a number of new libraries, includingPyTorch. Although originally they were meant to provide better supportfor those domain specific use cases, one can come to a conclusion, thatthey can actually have wider applications.In this talk, I’ll showcase the main ideas behind PyTorch - a relativelynew library focusing on usability and good integration with other Pythonpackages. I’ll cover some interesting use cases, ranging from ones morespecific to machine learning, to those more generally applicable inother scientific computing areas. I’ll also cover some recently addedfeatures, and talk a bit about our future roadmap.
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