Interview Questions Related to Convolutional Neural Networks
When I delved into the world of Convolutional Neural Network, many captivating questions came to my mind. I researched a lot to understand those insights.
In this blog, I have prepared some questions answers which may help you in cracking interviews related to CNN.
1. Why so many convolutional layers are added to CNN??
It’s like having detectives at different levels of expertise. The first detective (first layer) knows about basic stuff like edges. The next detective (second layer) combines what the first one found and looks for more complex patterns. As you add more detectives (more layers), they work together to understand the entire picture, from small details to big scenes.
So, why so many layers? It’s like having a team of detectives, each specializing in different aspects, working together to solve the mystery of the picture. They help the computer see not just the pieces but also how they fit together, making it really good at recognizing things in pictures.
2. Why do we add fully connected layer at the end in CNN?
In simple terms, Imagine you’re building a puzzle, and each piece of the puzzle represents a specific detail in an image (like a corner of an eye or a patch of fur in a cat picture). The convolutional layers in a CNN work like magnifying glasses, focusing on individual puzzle pieces (local features).
Now, to understand the whole picture, you need to connect these puzzle pieces and see how they fit together. Fully connected layers act like a clever friend who not only sees each puzzle piece but also figures out how they relate to one another. This friend can identify that certain combinations of puzzle pieces form larger patterns (like the entire face of a cat).
So, the combination of features by fully connected layers is like putting together the puzzle, recognizing complex patterns by understanding how different local features contribute to the bigger picture. It helps the network see not just individual details but also the relationships between them, allowing it to recognize more sophisticated and abstract patterns in the data.
In other words, while convolutional layers are great at zooming in on local details, fully connected layers help the network see the big picture and understand how all those details fit together. This combination allows the CNN to recognize intricate patterns and make sense of complex images.
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