ShuffleNet Implementation done

Creating this Pull request under OpenGenus#6077 - Hacktobefest. New feature of ShuffleNet architecture added. This fixes the issue no: OpenGenus#6297 .
nrjanjanam · Oct 23, 2021 · 767011a · 767011a
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diff --git a/code/artificial_intelligence/src/shufflenet_v1/README.md b/code/artificial_intelligence/src/shufflenet_v1/README.md
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+# ShuffeNet architecture
+Implementation of ShuffleNet architecture using CIFAR 10 dataset using Pytorch. The architecture is inspired from the original [ShuffleNet paper](https://arxiv.org/abs/1707.01083).
+
+One can replicate the same results by following these steps:
+1. Downloading this jupyter notebook on Google Colab. Alternatively, they can also load the dataset to their own computers instead of using Google Drive (as done in this notebook).
+2. Using GPU instead of CPU in google colab or similarly for your personal computer. In Google Colab, this can be done by going to Edit -> Notebook Settings -> Select GPU -> Save. The training happens faster with GPU as compared to CPU.
+3. Running all the cells **sequentially** in the order as in the notebook.
+4. It's done! You can go ahead and try the same architecture on different datasets.
+
diff --git a/code/artificial_intelligence/src/shufflenet_v1/ShuffleNet_Implementation_Using_Pytorch.ipynb b/code/artificial_intelligence/src/shufflenet_v1/ShuffleNet_Implementation_Using_Pytorch.ipynb