Update README.md

Author: AdityaTheDev

README.md

@@ -4,6 +4,9 @@ I built a Denoising Autoencoder to remove noise from the image. Image Denoising
 Denoising autoencoders are a stochastic version of standard autoencoders that reduces the risk of learning the identity function. Autoencoders are a class of neural networks used for feature selection and extraction, also called dimensionality reduction. In general, the more hidden layers in an autoencoder, the more refined this dimensional reduction can be. However, if an autoencoder has more hidden layers than inputs there is a risk the algorithm only learns the identity function during training, the point where the output simply equals the input, and then becomes useless.
 Denoising autoencoders attempt to get around this risk of identity-function affiliation by introducing noise, i.e. randomly corrupting input so that the autoencoder must then “denoise” or reconstruct the original input.
 
-I built an Autoencoder using the images of Brazilian model Adriana Lima which I found online. To the orginal image, I added GAUSSIAN NOISE and corrupted the pixels. Then passed the corrupted images as the input to the AutoEncoder and Predicted the output image which is denoised(Noise removed) image. The output images has removed a large volume of GUASSIAN NOISE which is apparent from the output given below.
+I built an Autoencoder using the images of Brazilian model Adriana Lima which I found online. To the orginal image, I added GAUSSIAN NOISE and corrupted the pixels. Then passed the corrupted images as the input to the AutoEncoder and Predicted the output image which is denoised(Noise removed) image. The output images has removed a large volume of GUASSIAN NOISE which is apparent from the output given below. 
+The first row image represents the orginal images,
+The second row represents the Noise added images,
+THe third row repressents the Denoised images(Predicted).
 
-![Image of Yaktocat](denoised.png)
+![Output images](denoised.png)