introduction to pytorch with deep learning to enable easieracceleratedand quality solutions for experiments in research and productsenterprises require large amount of abstraction that can do the heavy lifting for ground tasks this would help researchers and developers focus on the tasks that matterrather than investing the bulk of their time on basic operations deep learning frameworks and platforms provide fair abstraction on the ground complex tasks with simple functions that can be used as tools for solving larger problems by researchers and developers few popular choices are keraspytorchtensorflowmxnetcaffemicrosoft' cntketc what is pytorchpytorch is an open source machine learning and deep learning library developed by facebookinc it is python-basedas its name suggestsand aims to provide faster alternative/replacement to numpy (used in this examplesby providing seamless use of gpus and platform for deep learning that provides maximum flexibility and speed why pytorchrecommending pytorch is easy it provides an extremely easy to useextenddevelopand debug framework because it is pythonicit is easy for the software engineering community to embrace it is equally easy for researchers and developers to get tasks done pytorch also makes it easy for deep learning models to be productionized it is equipped with highperformance +runtime that developers can leverage for production environments while avoiding inference via python for most users who are familiar with python' numpy packagepytorch will be even easier to transition to overallpytorch provides an excellent framework and platform for researchers and developers to work on cutting-edge deep