PyTorch or TensorFlow? Comparing popular Machine Learning frameworks - KDnuggets

You may wonder, with TensorFlow remaining a prominent framework in the deep learning industry, why we bothered to write a PyTorch book of the Python Machine Learning series, Machine Learning with PyTorch and Scikit-Learn. As a matter of fact, PyTorch has become the most widely-used deep learning framework in the academic and research community. To examine this further, let me provide an up-to-date and more comprehensive comparison between PyTorch and TensorFlow. Supporting dynamic computational graphs is an advantage of PyTorch over TensorFlow. The computational graphs in PyTorch are built on-demand compared to their static TensorFlow counterparts. This makes PyTorch more debug-friendly: you can execute the code line by line while having full access to all variables.