Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch is one such library.
In the last few weeks, I have been dabbling a bit in PyTorch. I have been blown away by how easy it is to grasp.
Among the various deep learning libraries I have used till date – PyTorch has been the most flexible and effortless of them all. In this article, we will explore PyTorch with a more hands-on approach, covering the basics along with a case study.
We will also compare a neural network built from scratch in both numpy and PyTorch to see their similarities in implementation. Note – This article assumes that you have a basic understanding of deep learning.
If you want to get up to speed with deep learning, please go through this article first. PyTorch’s creators say that they have a philosophy – they want to be imperative. Read more from analyticsvidhya.com…
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