Autograd: The Best Machine Learning Library You're Not Using? - KDnuggets

Ask a group of deep learning practitioners for their programming language of choice and you'll undoubtedly hear a lot about Python. Ask about their go-to machine learning library, on the other hand, and you're likely to get a picture of a two library system with a mix of TensorFlow and PyTorch. While there are plenty of people that may be familiar with both, in general commercial applications in machine learning (ML) tend to be dominated by the use of TensorFlow, while research projects in artificial intelligence/ML mostly use PyTorch. Although there's significant convergence between the two libraries with the introduction of eager execution by default in TensorFlow 2.0 released last year, and the availability of building static executable models using Torchscript, most seem to stick to one or the other for the most part. While the general consensus seems to be that you should pick TensorFlow for its better deployment and edge support if you want to join a company, and PyTorch for flexibility and readability if you want to work in academic research, there's more to the world of AI/ML libraries than just PyTorch and TensorFlow.