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add pic to readme
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README.md

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@@ -50,6 +50,8 @@ Elaborating further:
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If you use numpy, then you have used Tensors (a.k.a ndarray).
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<p align=center><img width="30%" src="docs/source/_static/img/tensor_illustration.png" /></p>
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PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate
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compute by a huge amount.
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While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date.
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You get the best of speed and flexibility for your crazy research.
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<p align=center><img width="80%" src="docs/source/_static/img/dynamic_graph.gif" /></p>
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### Python first
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PyTorch is not a Python binding into a monolothic C++ framework.
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