Getting into Convolution Neural Networks
Welcome everyone! This is my sixth writing on my journey of Completing the Deep Learning Nanodegree in a month! I’ve done 25% of the third module out of a total of six modules of the degree. Today’s topic was Convolutional Neural Networks, CNNs.
Day- 7
I finished the Lesson of CNNs today and I noted some key points.
Need of CNNs
We have done MLPs till now and yeah, it does a fine job on training models on the MNIST data, only I’ve dealt with till now. But when we talk about models which perform tasks like image processing, or the models that drive a self-drive car, or perhaps our face recognition systems, then MLPs don’t do such a good job. The thing is that with MLPs, we can’t really generalize a model because the model would fail to classify an upside down image, or an image that is tilted a bit to its right, etc. This shows that the MLPs are not so good for Images. What now? We use Convolutional Neural Networks.
Main difference
The main difference between CNNs and MLPs is that MLPs require vectors as inputs and when we have to deal with images, we flatten he image pixels into a 1D vector and then pass it. What we don’t realize is that during flattening the image, we lose some valuable information about the image which later…
