Pytorch is a Python-based machine learning library. It is widely used in deep learning fields such as computer vision and natural language processing. It is currently a deep learning framework that competes with TensorFlow and is very popular in academic circles.
It mainly provides the following two core functions:
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Support GPU accelerated tensor calculation.
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It is convenient to optimize the automatic differentiation mechanism of the model.
The main advantages of Pytorch:
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Concise and easy to understand: Pytorch's API design is quite concise and consistent. Basically it is tensor, autograd, nn three-level packaging. It is very easy to learn. There is a paragraph like this, saying that TensorFlow's design philosophy is Make it complicated, Keras's design philosophy is Make it complicated and hide it, and Pytorch's design philosophy is Keep it simple and stupid.
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Easy to debug: Pytorch uses dynamic graphs, which can be debugged like ordinary Python code. Unlike TensorFlow, Pytorch's error reporting instructions are usually easy to understand. There is a paragraph like this, saying that you can never find the reason for its error from the error description of TensorFlow.
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Powerful and efficient: Pytorch provides a very rich model components that can quickly realize ideas. And it runs very fast. At present, most of the paper related to deep learning is implemented with Pytorch. Some researchers say that after switching from using TensorFlow to using Pytorch, their sleep is much better, their hair is thicker than before, and their skin is smoother than before.
As the saying goes, the Pytorch building also has its foundation.
The core concepts at the bottom of Pytorch are tensors, dynamic calculation graphs and automatic differentiation.
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