Why is PyTorch better than TensorFlow?
The question is about Tensorflow
PyTorch typically gains an advantage over TensorFlow in research and prototyping based on its dynamic computation graph since it allows for much more intuitive and flexible model building. Due to the dynamic approach, debugging and experimenting with models is becoming easier. Therefore, PyTorch finds a broad circle of followers among researchers and practitioners involved in natural language processing and computer vision. PyTorch’s Pythonic nature and smooth integration with other Python libraries make it more productive for developers and easier on the eyes. In contrast, TensorFlow is based on static graphs, which are much wordier. Apart from this, PyTorch has won strong support in the community and academia, where many research papers and pre-trained models are ready for use. While TensorFlow excels in the field of production, PyTorch remains a lightweight alternative yet easy to adapt in a research setting.
