PyTorch is an open source machine learning framework based on the Torch library, used for applications such as computer vision and natural language processing,[6] primarily developed by Facebook's AI Research lab (FAIR). It is free and open-source software released under the Modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface.
So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development. The main difference between them is that PyTorch may feel more “pythonic” and has an object-oriented approach while TensorFlow has several options from which you may choose.
PyTorch is designed to provide good flexibility and high speeds for deep neural network implementation. PyTorch is different from other deep learning frameworks in that it uses dynamic computation graphs.
PyTorch is much easier to learn than any other deep learning library since it doesn't deviate too much from standard programming approaches. PyTorch's documentation is also fantastic and quite beneficial for novices.
PyTorch wraps the same C back end in a Python interface. But it's more than just a wrapper. Developers built it from the ground up to make models easy to write for Python programmers. The underlying, low-level C and C++ code is optimized for running Python code.
We learn because PyTorch is relatively easy to understand and, it feels more natural, native and in line with Python code. For anyone who has worked with Python, they'll feel at home with everything with PyTorch, such as loss functions, optimizers, transformations, data loaders and many more classes.
