Update README.md

Author: abhisheksambyal

README.md

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 In this project, nuances of the autoencoder training were looked over.
 1. Autoencoder end-to-end training for classifying MNIST dataset. [[Notebook01](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/01_Autoencoder_Classifier_End_to_End_Training.ipynb)]
 2. Autoencoder Layer Wise Pre-training (Stacking) for Fashion-MNIST. [[Notebook02](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/02_Autoencoder_Classifier_Layer_Wise_Pre_training_Fashion_MNIST.ipynb)]
-3. [DRIVE (Digital Retinal Images for Vessel Extractions](https://www.isi.uu.nl/Research/Databases/DRIVE/) dataset patchwise segmentation using Autoencoder. [[Notebook03](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb)]
+3. [DRIVE (Digital Retinal Images for Vessel Extractions)](https://www.isi.uu.nl/Research/Databases/DRIVE/) dataset patchwise segmentation using Autoencoder. [[Notebook03](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/03_Retinal_Vessel_Detection_Autoencoder_Segmentation.ipynb)]
 4. Sparse Denoising Autoencoder (SDAE) for classification of MNIST dataset. [[Notebook04](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/04_Sparse_Autoencoder_MNIST_Classification.ipynb), [Notebook05](https://github.com/abhigoogol/Autoencoders-using-Pytorch/blob/master/05_Denoising_Autoencoder_MNIST_Classification.ipynb)]
 
 ## Built With