status formatting

Author: sgrvinod

README.md

@@ -16,11 +16,11 @@ In each tutorial, we will focus on a specific application or area of interest by
 
 Application | Paper | Tutorial | Also Learn About | Status
 :---: | :---: | :---: | :---: | :---:
-Image Captioning | [_Show, Attend, and Tell_](https://arxiv.org/abs/1502.03044) | [a PyTorch Tutorial to Image Captioning](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning) | &bull; encoder-decoder architecture<br/><br/>&bull; attention <br/><br/>&bull; transfer learning <br/><br/>&bull; beam search | 🟢 *complete*
-Sequence Labeling | [_Empower Sequence Labeling with Task-Aware Neural Language Model_](https://arxiv.org/abs/1709.04109) | [a PyTorch Tutorial to Sequence Labeling](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling) | &bull; language models<br/><br/>&bull; character RNNs <br/><br/>&bull; multi-task learning <br/><br/>&bull; conditional random fields <br/><br/>&bull; Viterbi decoding <br/><br/>&bull; highway networks | 🟢 *complete*
-Object Detection | [_SSD: Single Shot MultiBox Detector_](https://arxiv.org/abs/1512.02325) | [a PyTorch Tutorial to Object Detection](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection) | &bull; single-shot detection<br/><br/>&bull; multiscale feature maps <br/><br/>&bull; priors <br/><br/>&bull; multibox <br/><br/>&bull; hard negative mining <br/><br/>&bull; non-maximum suppression | 🟢 *complete*
-Text Classification | [_Hierarchical Attention Networks for Document Classification_](https://www.semanticscholar.org/paper/Hierarchical-Attention-Networks-for-Document-Yang-Yang/1967ad3ac8a598adc6929e9e6b9682734f789427) | [a PyTorch Tutorial to Text Classification](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Text-Classification) | &bull; hierarchical attention | 🟡 *code complete*
-Super-Resolution | [_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_](https://arxiv.org/abs/1609.04802) | [a PyTorch Tutorial to Super-Resolution](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution) | &bull; **GANs**! — this is also a GAN tutorial! <br/><br/>&bull; residual connections <br/><br/>&bull; sub-pixel convolution <br/><br/>&bull; perceptual loss | 🟢 *complete*
-Machine Translation | [_Attention Is All You Need_](https://arxiv.org/abs/1706.03762) | [a PyTorch Tutorial to Machine Translation](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation) | &bull; **transformers**! — this is also a transformer tutorial! <br/><br/>&bull; multi-head attention <br/><br/>&bull; positional embeddings <br/><br/>&bull; encoder-decoder architecture <br/><br/>&bull; byte pair encoding <br/><br/>&bull; beam search | 🟢 *complete*
-Semantic Segmentation | [_SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers_](https://arxiv.org/abs/2105.15203) | a PyTorch Tutorial to Semantic Segmentation | N/A | 🔴 *planned*
+Image Captioning | [_Show, Attend, and Tell_](https://arxiv.org/abs/1502.03044) | [a PyTorch Tutorial to Image Captioning](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Image-Captioning) | &bull; encoder-decoder architecture<br/><br/>&bull; attention <br/><br/>&bull; transfer learning <br/><br/>&bull; beam search | 🟢<br/>*complete*
+Sequence Labeling | [_Empower Sequence Labeling with Task-Aware Neural Language Model_](https://arxiv.org/abs/1709.04109) | [a PyTorch Tutorial to Sequence Labeling](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling) | &bull; language models<br/><br/>&bull; character RNNs <br/><br/>&bull; multi-task learning <br/><br/>&bull; conditional random fields <br/><br/>&bull; Viterbi decoding <br/><br/>&bull; highway networks | 🟢<br/>*complete*
+Object Detection | [_SSD: Single Shot MultiBox Detector_](https://arxiv.org/abs/1512.02325) | [a PyTorch Tutorial to Object Detection](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection) | &bull; single-shot detection<br/><br/>&bull; multiscale feature maps <br/><br/>&bull; priors <br/><br/>&bull; multibox <br/><br/>&bull; hard negative mining <br/><br/>&bull; non-maximum suppression | 🟢<br/>*complete*
+Text Classification | [_Hierarchical Attention Networks for Document Classification_](https://www.semanticscholar.org/paper/Hierarchical-Attention-Networks-for-Document-Yang-Yang/1967ad3ac8a598adc6929e9e6b9682734f789427) | [a PyTorch Tutorial to Text Classification](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Text-Classification) | &bull; hierarchical attention | 🟡<br/>*code complete*
+Super-Resolution | [_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network_](https://arxiv.org/abs/1609.04802) | [a PyTorch Tutorial to Super-Resolution](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution) | &bull; **GANs**! — this is also a GAN tutorial! <br/><br/>&bull; residual connections <br/><br/>&bull; sub-pixel convolution <br/><br/>&bull; perceptual loss | 🟢<br/>*complete*
+Machine Translation | [_Attention Is All You Need_](https://arxiv.org/abs/1706.03762) | [a PyTorch Tutorial to Machine Translation](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Machine-Translation) | &bull; **transformers**! — this is also a transformer tutorial! <br/><br/>&bull; multi-head attention <br/><br/>&bull; positional embeddings <br/><br/>&bull; encoder-decoder architecture <br/><br/>&bull; byte pair encoding <br/><br/>&bull; beam search | 🟢<br/>*complete*
+Semantic Segmentation | [_SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers_](https://arxiv.org/abs/2105.15203) | a PyTorch Tutorial to Semantic Segmentation | N/A | 🔴<br/>*planned*