What are the differences between TensorFlow and PyTorch?

TensorFlow and PyTorch are two popular deep learning frameworks with some differences in their design and usage.

1. Static computation graph vs dynamic computation graph: TensorFlow utilizes a static computation graph, where users define the graph first before executing it. On the other hand, PyTorch employs a dynamic computation graph which is constructed during runtime, making it more flexible and intuitive.
2. API design: TensorFlow’s API is more extensive and complex, while PyTorch’s API is simpler and more intuitive, making it easier to learn and use.
3. Community support: TensorFlow has a larger user base and more documentation support, while PyTorch is more popular in academic and research fields.
4. Front-end languages: TensorFlow mainly uses Python as its front-end language, while PyTorch supports both Python and Lua for front-end languages.

In general, TensorFlow is better suited for large-scale production environments and engineering applications, while PyTorch is more suitable for research and experiments due to its more intuitive API design and dynamic computation graph that better supports rapid iteration and experimentation.