PyTorch is a Python-based scientific computing package that uses the power of graphic allotments. It is also one of the deep learning research platforms used to provide maximum flexibility and speed. Two of the most high-level features; to create deep neural networks in tenor calculations and tape-based autograd systems with strong GPU acceleration support.
There are many Python libraries with the potential to change deep learning and the way artificial intelligence is realized, 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 : Offers an easy-to-use API, so it's very simple to run like Python.
- Pythonic in nature : This pythonic library integrates seamlessly with Python data science. This allows you to take advantage of all services and functionality offered by the Python environment.
- Computational charts : PyTorch provides an excellent platform that offers dynamic computational graphics, so you can change them during runtime. This is useful when you don't know how much memory it takes to create a neural network model.
Why Use PyTorch?
Anyone working 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. Probably pytorch is TensorFlow's biggest ever rival and the deep learning and artificial intelligence library, which is currently widely preferred in the research community.
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