2 Convolutional Neural Networks: A Bottom-Up Approach
Karan Maheshwari, Aditya Shaha, Dhruv Arya, Rajkumar Rajasekaran, B. K. Tripathy
Abstract
A lot has changed in the world since the inception of the so-called deep learning (DL) era. Unlike conventional machine learning algorithms, where human learns more of feature extraction than machine, which just crunches numbers, DL algorithms are quite promising as they have revolutionized the way we deal with data and have become adept in taking human-like decisions. Based on the powerful notion of artificial neural networks, these learning algorithms learn to represent data or, if we rephrase, can extract features from raw data. DL has paved way for end-to-end systems where one learning algorithm does all the tasks. Convolutional neural networks have earned a lot of fame lately, especially in the domain of computer vision where in some cases its performance has beaten that of humans. A lot of work has been done on convNets and in this chapter we will demystify how convolutional neural networks work and will illustrate using one novel application in the field of astronomy where we will do galaxy classification using raw images as input and classify them based on its shape. In addition to this we will investigate what features the network is learning. We will also discuss how DL superseded other forms of learning and some recent algorithmic innovations centered on convolutional neural nets.