PyTorch is a Python-based scientific computing suite that uses the power of graphics processing units. It is also one of the deep learning research platforms used to provide maximum flexibility and speed. Two of the highest-end features are; to create deep neural networks in tensor calculations and a tape-based autograde system with powerful GPU acceleration support.
Deep Learning and there are many Python libraries that have the potential to change the way AI is performed, and PyTorch is such a library. One of the main reasons behind PyTorch's success is that it is completely Pythonic and can seamlessly create neural network models.
PyTorch Features :
- Simple Interface : It offers an easy-to-use API, so it is very simple to run like Python.
- Pythonic in nature : This library, which is Pythonic, integrates seamlessly with Python data science. This allows it to take advantage of all the services and functionality offered by the Python environment.
- Computational charts : PyTorch provides an excellent platform that offers dynamic computational charts, so you can change them during runtime. This is very useful when you do not know how much memory it will take to create a neural network model.
Why Use PyTorch?
Everyone who works in Deep Learning and artificial intelligence has probably worked with TensorFlow, Google's most popular open source library. However, the latest deep learning freamwork — PyTorch solves important problems in terms of research studies. Probably PyTorch is TensorFlow's biggest competitor to date and is currently the much-preferred library of deep learning and artificial intelligence in the research community.
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