Skip to main content

Why use CNN instead of normal Feed forward Network?

    While we can use a simple Feed Forward Network, we use Convolutional Neural Network... What is the need for such a network?

    I took a picture of me with my mobile camera which has 12 MP resolution ( Poco F1). I imported it as numpy array. Number of pixel in that image was 2756160. I have nearly 3 million pixel! If i use a Feed Forward Network it means i have 3 million features! I will require more number of data. A 10 million photos will satisfy my need. Also this would require a large memory and long time for training!

   We can reduce the dimension? PCA? PCA of 3 million X 10 million will still be a great problem. Moreover we cannot get 10 million photos. We have to train better with small amount of data.

   How else can we reduce the dimension? Feature extraction is a good option, we can have multiple filters for a image and use the results of filter as the feature. This significantly reduces our data. 128 filters (of size 3x3) applied to my image will reduce it into a  128 images of size 3x3. This is what a Convolutional Neural Network is! The  problem gets reduced in dimension and difficulty...
The size of the image was found to be 2756160.

Comments

Post a Comment

Popular posts from this blog

CodeSignal - Almost Strictly Increasing Sequence

I came across an interesting question in Code Signal.   "Given a sequence of integers as an array, determine whether it is possible to obtain a strictly increasing sequence by removing no more than one element from the array."                                                                                                         It is strictly increasing if every element in the array is greater than its successor.     For a strictly increasing sequence we can check for each element whether it is greater than the next. In that case we can come to a conclusion that this sequence is not strictl...
Post a Comment
Read more

Deep Learning (Goodfellow et al) - Chapter 3 review

 After a long time starting my review with the third chapter!    This chapter jots down all necessary concepts of Probability and Information theory in regards to  the future scope of the book. While probability theory is introduced as a mathematical framework for representing uncertain statements, the information theory is introduced as the quantifying concept for the uncertainty. For deeper understanding, an additional resources would be good to consult like Jaynes(2003).      Truly computer science does not require the study of uncertainty until the machines start to learn from data. Because data and learning processes have uncertainty. The authors describe about the source of uncertainty.  There are three possible sources of uncertainty: - Inherent stochasticity in the system. - Incomplete Observability. - Incomplete Modeling.                                  ...
Post a Comment
Read more