Add 5 more questions with answers

README.md

@@ -63,6 +63,21 @@ Imagine a network with random initialized weights ( or normalised ) and almost 5
 
 #### 20) Reverse a linked list in place. [[src](https://www.reddit.com/r/computervision/comments/7gku4z/technical_interview_questions_in_cv/)]
 
+#### 21) What is data normalization and why do we need it? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
+Data normalization is very important preprocessing step, used to rescale values to fit in a specific range to assure better convergence during backpropagation. In general, it boils down to subtracting the mean of each data point and dividing by its standard deviation. If we don't do this then some of the features (those with high magnitude) will be weighted more in the cost function (if a higher-magnitude feature changes by 1%, then that change is pretty big, but for smaller features it's quite insignificant). The data normalization makes all features weighted equally.
+
+#### 22) Why do we use convolutions for images rather than just FC layers? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
+Firstly, convolutions preserve, encode, and actually use the spatial information from the image. If we used only FC layers we would have no relative spatial information. Secondly, Convolutional Neural Networks (CNNs) have a partially built-in translation in-variance, since each convolution kernel acts as it's own filter/feature detector.
+
+#### 23) What makes CNNs translation invariant? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
+As explained above, each convolution kernel acts as it's own filter/feature detector. So let's say you're doing object detection, it doesn't matter where in the image the object is since we're going to apply the convolution in a sliding window fashion across the entire image anyways.
+
+#### 24) Why do we have max-pooling in classification CNNs? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
+for a role in Computer Vision. Max-pooling in a CNN allows you to reduce computation since your feature maps are smaller after the pooling. You don't lose too much semantic information since you're taking the maximum activation. There's also a theory that max-pooling contributes a bit to giving CNNs more translation in-variance. Check out this great video from Andrew Ng on the [benefits of max-pooling](https://www.coursera.org/learn/convolutional-neural-networks/lecture/hELHk/pooling-layers).
+
+#### 25) Why do segmentation CNNs typically have an encoder-decoder style / structure? [[src](http://houseofbots.com/news-detail/2849-4-data-science-and-machine-learning-interview-questions)]
+The encoder CNN can basically be thought of as a feature extraction network, while the decoder uses that information to predict the image segments by "decoding" the features and upscaling to the original image size.
+
 ## Contributions
 Contributions are most welcomed.
  1. Fork the repository.