TensorFlow using Static Graph concept that means the user first has to define the computation graph of the model and then run the ML model. On the other hand, PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating the graphs. You can check out this TensorFlow Tutorial by Intellipaat to learn the TensorFlow framework.
You can watch this video to understand more:
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