TensorFlow deepens its advantages in the Artificial Intelligence modeling wars
Artificial intelligence developers to use TensorFlow as their primary open source tool or alongside PyTorch, in which they develop most of their ML (machine learning), deep learning, and NLP (natural language processing) models where TensorFlow remains the dominant AI modeling framework.
TensorFlow and PyTorch can be quite effective, especially if there is already a trained model similar to what you need in the framework’s model library. It builds on the older Torch and Caffe2 frameworks; PyTorch uses Python as its scripting language, and uses an evolved Torch C/CUDA back-end. The production features of Caffe2 are being incorporated into the PyTorch project.
It is software used for dataflow and differentiable programming across a range of tasks. It supports every AI development, training, and deployment scenario you can imagine.
TensorFlow dominance among working data scientists may be waning, nevertheless, PyTorch remains a strong second choice. It remains by far the top AI modeling framework, not just in adoption and maturity, but in terms of the sheer depth and breadth of the stack in supporting every conceivable AI development, training, and deployment scenario.
Although PyTorch strong for 80 percent of the core AI, deep learning, and machine learning challenges, has a long way to go before it arrives at feature parity.
