Why CNN

Some History for CNN

Hubel & Wisel in 1959 to 1968 experiments on cats cortex found that there are a topographical mapping in the cortex and that the neurons has hireical organization from simple to complex.

In 1998, Yann Lecun gives the paper Gradient-based learning applied to document recognition that introduced the Convolutional neural networks. It was good for recognizing zip letters but couldn’t run on a more complex examples.

In 2012 AlexNet used the same Yan Lecun architecture and won the image net challenge. The difference from 1998 is that now we have a large data sets that can be used and the power of the GPUs solved a lot of performance problems.

Starting from 2012 there are CNN that are used for various tasks like:

Image classification.
Image retrieval: Extracting features using a NN and then do a similarity matching.
Object detection.
Segmentation: Each pixel in an image takes a label.
Face recognition.
Pose recognition.
Medical images.
Playing Atari games with reinforcement learning.