diff --git a/README.md b/README.md
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@@ -96,7 +96,7 @@ This library contains a PyTorch implementation of the SO(3) equivariant CNNs for
 20. [pytorch-text-recognition](https://github.com/s3nh/pytorch-text-recognition): Text recognition combo - CRAFT + CRNN.
 21. [facenet-pytorch](https://github.com/timesler/facenet-pytorch): Pretrained Pytorch face detection and recognition models ported from davidsandberg/facenet.
 22. [detectron2](https://github.com/facebookresearch/detectron2): Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
-23. [vedaseg](https://github.com/Media-Smart/vedaseg): A semantic segmentation framework by pyotrch
+23. [vedaseg](https://github.com/Media-Smart/vedaseg): A semantic segmentation framework by pytorch
 24. [ClassyVision](https://github.com/facebookresearch/ClassyVision): An end-to-end PyTorch framework for image and video classification.
 25. [detecto](https://github.com/alankbi/detecto):Computer vision in Python with less than 10 lines of code
 26. [pytorch3d](https://github.com/facebookresearch/pytorch3d): PyTorch3D is FAIR's library of reusable components for deep learning with 3D data pytorch3d.org
@@ -356,7 +356,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 69. [Deep Learning with PyTorch](https://www.manning.com/books/deep-learning-with-pytorch): Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch, the book includes a case study: building an algorithm capable of detecting malignant lung tumors using CT scans.
 70. [Serverless Machine Learning in Action with PyTorch and AWS](https://www.manning.com/books/serverless-machine-learning-in-action): Serverless Machine Learning in Action is a guide to bringing your experimental PyTorch machine learning code to production using serverless capabilities from major cloud providers like AWS, Azure, or GCP.
 71. [LabML NN](https://github.com/lab-ml/nn): A collection of PyTorch implementations of neural networks architectures and algorithms with side-by-side notes.
-72. [Run your PyTorch Example Fedarated with Flower](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated): This example demonstrates how an already existing centralized PyTorch machine learning project can be federated with Flower. A Cifar-10 dataset is used together with a convolutional neural network (CNN).
+72. [Run your PyTorch Example federated with Flower](https://github.com/adap/flower/tree/main/examples/pytorch_from_centralized_to_federated): This example demonstrates how an already existing centralized PyTorch machine learning project can be federated with Flower. A Cifar-10 dataset is used together with a convolutional neural network (CNN).
 
 ## Paper implementations
 
@@ -370,7 +370,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 8. [pytorch_NEG_loss](https://github.com/analvikingur/pytorch_NEG_loss): NEG loss implemented in pytorch.  
 9. [pytorch_RVAE](https://github.com/analvikingur/pytorch_RVAE): Recurrent Variational Autoencoder that generates sequential data implemented in pytorch.   
 10. [pytorch_TDNN](https://github.com/analvikingur/pytorch_TDNN): Time Delayed NN implemented in pytorch.  
-11. [eve.pytorch](https://github.com/moskomule/eve.pytorch): An implementation of Eve Optimizer, proposed in Imploving Stochastic Gradient Descent with Feedback, Koushik and Hayashi, 2016.  
+11. [eve.pytorch](https://github.com/moskomule/eve.pytorch): An implementation of Eve Optimizer, proposed in improving Stochastic Gradient Descent with Feedback, Koushik and Hayashi, 2016.  
 12. [e2e-model-learning](https://github.com/locuslab/e2e-model-learning): Task-based end-to-end model learning.  
 13. [pix2pix-pytorch](https://github.com/mrzhu-cool/pix2pix-pytorch): PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".   
 14. [Single Shot MultiBox Detector](https://github.com/amdegroot/ssd.pytorch): A PyTorch Implementation of Single Shot MultiBox Detector.  
@@ -425,7 +425,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 63. [pointnet.pytorch](https://github.com/fxia22/pointnet.pytorch): pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593  
 64. **[pytorch-playground](https://github.com/aaron-xichen/pytorch-playground): Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)**.
 65. [pytorch-dnc](https://github.com/jingweiz/pytorch-dnc): Neural Turing Machine (NTM) & Differentiable Neural Computer (DNC) with pytorch & visdom. 
-66. [pytorch_image_classifier](https://github.com/jinfagang/pytorch_image_classifier): Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.  
+66. [pytorch_image_classifier](https://github.com/jinfagang/pytorch_image_classifier): Minimal But Practical Image Classifier pipeline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.  
 67. [mnist-svhn-transfer](https://github.com/yunjey/mnist-svhn-transfer): PyTorch Implementation of CycleGAN and SGAN for Domain Transfer (Minimal).
 68. [pytorch-yolo2](https://github.com/marvis/pytorch-yolo2): pytorch-yolo2
 69. [dni](https://github.com/andrewliao11/dni.pytorch): Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch
@@ -476,7 +476,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 114. [ORN](https://github.com/ZhouYanzhao/ORN): A PyTorch implementation of the paper "Oriented Response Networks" in CVPR 2017. 
 115. [pytorch-maml](https://github.com/katerakelly/pytorch-maml): PyTorch implementation of MAML: arxiv.org/abs/1703.03400
 116. [pytorch-generative-model-collections](https://github.com/znxlwm/pytorch-generative-model-collections):  Collection of generative models in Pytorch version.
-117. [vqa-winner-cvprw-2017](https://github.com/markdtw/vqa-winner-cvprw-2017): Pytorch Implementation of winner from VQA Chllange Workshop in CVPR'17. 
+117. [vqa-winner-cvprw-2017](https://github.com/markdtw/vqa-winner-cvprw-2017): Pytorch Implementation of winner from VQA challenge Workshop in CVPR'17. 
 118. [tacotron_pytorch](https://github.com/r9y9/tacotron_pytorch):  PyTorch implementation of Tacotron speech synthesis model. 
 119. [pspnet-pytorch](https://github.com/Lextal/pspnet-pytorch): PyTorch implementation of PSPNet segmentation network
 120. [LM-LSTM-CRF](https://github.com/LiyuanLucasLiu/LM-LSTM-CRF): Empower Sequence Labeling with Task-Aware Language Model http://arxiv.org/abs/1709.04109
@@ -521,7 +521,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 159. [SFD_pytorch](https://github.com/clcarwin/SFD_pytorch): A PyTorch Implementation of Single Shot Scale-invariant Face Detector.
 160. [GradientEpisodicMemory](https://github.com/facebookresearch/GradientEpisodicMemory): Continuum Learning with GEM: Gradient Episodic Memory. https://arxiv.org/abs/1706.08840
 161. [DeblurGAN](https://github.com/KupynOrest/DeblurGAN): Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks.
-162. [StarGAN](https://github.com/yunjey/StarGAN): StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Tranlsation.
+162. [StarGAN](https://github.com/yunjey/StarGAN): StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image translation.
 163. [CapsNet-pytorch](https://github.com/adambielski/CapsNet-pytorch): PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules.
 164. [CondenseNet](https://github.com/ShichenLiu/CondenseNet): CondenseNet: An Efficient DenseNet using Learned Group Convolutions.
 165. [deep-image-prior](https://github.com/DmitryUlyanov/deep-image-prior): Image restoration with neural networks but without learning.
@@ -539,7 +539,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 177. [nmn-pytorch](https://github.com/HarshTrivedi/nmn-pytorch): Neural Module Network for VQA in Pytorch.
 178. [bytenet](https://github.com/kefirski/bytenet): Pytorch implementation of bytenet from "Neural Machine Translation in Linear Time" paper
 179. [bottom-up-attention-vqa](https://github.com/hengyuan-hu/bottom-up-attention-vqa): vqa, bottom-up-attention, pytorch
-180. [yolo2-pytorch](https://github.com/ruiminshen/yolo2-pytorch): The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorch as the developing framework to increase productivity, and utilize ONNX to convert models into Caffe 2 to benifit engineering deployment.
+180. [yolo2-pytorch](https://github.com/ruiminshen/yolo2-pytorch): The YOLOv2 is one of the most popular one-stage object detector. This project adopts PyTorch as the developing framework to increase productivity, and utilize ONNX to convert models into Caffe 2 to benefit engineering deployment.
 181. [reseg-pytorch](https://github.com/Wizaron/reseg-pytorch): PyTorch Implementation of ReSeg (arxiv.org/pdf/1511.07053.pdf)
 182. [binary-stochastic-neurons](https://github.com/Wizaron/binary-stochastic-neurons): Binary Stochastic Neurons in PyTorch.
 183. [pytorch-pose-estimation](https://github.com/DavexPro/pytorch-pose-estimation): PyTorch Implementation of Realtime Multi-Person Pose Estimation project.
@@ -585,10 +585,10 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 223. [VRNN](https://github.com/emited/VariationalRecurrentNeuralNetwork): Pytorch implementation of the Variational RNN (VRNN), from A Recurrent Latent Variable Model for Sequential Data.
 224. [flow](https://github.com/emited/flow): Pytorch implementation of ICLR 2018 paper Deep Learning for Physical Processes: Integrating Prior Scientific Knowledge.
 225. [deepvoice3_pytorch](https://github.com/r9y9/deepvoice3_pytorch): PyTorch implementation of convolutional networks-based text-to-speech synthesis models
-226. [psmm](https://github.com/elanmart/psmm): imlementation of the the Pointer Sentinel Mixture Model, as described in the paper by Stephen Merity et al.
+226. [psmm](https://github.com/elanmart/psmm): implementation of the the Pointer Sentinel Mixture Model, as described in the paper by Stephen Merity et al.
 227. [tacotron2](https://github.com/NVIDIA/tacotron2): Tacotron 2 - PyTorch implementation with faster-than-realtime inference.
 228. [AccSGD](https://github.com/rahulkidambi/AccSGD): Implements pytorch code for the Accelerated SGD algorithm.
-229. [QANet-pytorch](https://github.com/hengruo/QANet-pytorch): an implementation of QANet with PyTorch (EM/F1 = 70.5/77.2 after 20 epoches for about 20 hours on one 1080Ti card.)
+229. [QANet-pytorch](https://github.com/hengruo/QANet-pytorch): an implementation of QANet with PyTorch (EM/F1 = 70.5/77.2 after 20 epochs for about 20 hours on one 1080Ti card.)
 230. [ConvE](https://github.com/TimDettmers/ConvE): Convolutional 2D Knowledge Graph Embeddings
 231. [Structured-Self-Attention](https://github.com/kaushalshetty/Structured-Self-Attention): Implementation for the paper A Structured Self-Attentive Sentence Embedding, which is published in ICLR 2017: arxiv.org/abs/1703.03130 .
 232. [graphsage-simple](https://github.com/williamleif/graphsage-simple): Simple reference implementation of GraphSAGE.
@@ -612,7 +612,7 @@ Demonstration of training a small ResNet on CIFAR10 to 94% test accuracy in 79 s
 250. [LOLA_DiCE](https://github.com/alexis-jacq/LOLA_DiCE): Pytorch implementation of LOLA (arxiv.org/abs/1709.04326) using DiCE (arxiv.org/abs/1802.05098)
 251. [generative-query-network-pytorch](https://github.com/wohlert/generative-query-network-pytorch): Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"
 252. [pytorch_hmax](https://github.com/wmvanvliet/pytorch_hmax): Implementation of the HMAX model of vision in PyTorch.
-253. [FCN-pytorch-easiest](https://github.com/yunlongdong/FCN-pytorch-easiest): trying to be the most easiest and just get-to-use pytorch implementation of FCN (Fully Convolotional Networks)
+253. [FCN-pytorch-easiest](https://github.com/yunlongdong/FCN-pytorch-easiest): trying to be the most easiest and just get-to-use pytorch implementation of FCN (Fully convolutional Networks)
 254. [transducer](https://github.com/awni/transducer): A Fast Sequence Transducer Implementation with PyTorch Bindings.
 255. [AVO-pytorch](https://github.com/artix41/AVO-pytorch): Implementation of Adversarial Variational Optimization in PyTorch.
 256. [HCN-pytorch](https://github.com/huguyuehuhu/HCN-pytorch): A pytorch reimplementation of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.