Update README.md

README.md

@@ -19,7 +19,7 @@ datasets:
 
 A hands-on guide to building a deep-learning model that cleans noisy images, improving downstream classification tasks.
 
-When I began experimenting with image-classification projects, I quickly realized how sensitive models are to noise. Small imperfections—sensor noise, compression artifacts, random pixel disturbances—could drastically reduce performance.
+When I began experimenting with image-classification projects, I quickly realized how sensitive models are to noise. Small imperfections, sensor noise, compression artifacts, random pixel disturbances, could drastically reduce performance.
 
 Instead of training classifiers directly on noisy images, I decided to build a **preprocessing model**: one whose sole purpose is to take a noisy input and output a cleaner version. This approach allows classifiers to focus on meaningful patterns rather than irrelevant distortions.
 
@@ -144,7 +144,7 @@ After training, comparing:
 * Denoised output
 * Original image
 
-The autoencoder effectively removes noise while keeping key structures intact—ideal for lightweight models and MNIST.
+The autoencoder effectively removes noise while keeping key structures intact-ideal for lightweight models and MNIST.
 
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