Assignments and Supplemental Reading Materials

Now that you have built a project and completed the quiz, you are ready to move to the next step: exploring some extra supplemental materials and completing the assignments to gain a better understanding of the topics we discussed.

Supplemental reading materials

1. Gradient-Based Learning Applied to Document Recognition by Yann LeCun and others. LeCun is considered as one of the founding fathers of the field of Convolutional Neural Networks, and he was the first person to head the Facebook AI Research organization in New York.

2. Understanding Convolutional Neural Networks with a Mathematical Model by C. C. Jay Kuo.

3. If you’re looking to dig even, you can move on to the 2015 paper written by Kaiming, titled Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. In this paper, the authors propose a modified form of the rectifier function called the Parametric Rectified Linear Unit (PReLU). It’s quite an interesting read if you’re into the topic.

4. Check out this 2010 paper, titled Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition, by Dominik Scherer and others from the University of Bonn. It’s a pretty simple read, only 10 pages long, and will boil down the concept of pooling for you. You can even skip the second part titled “Related Work” if you find it irrelevant to what you want to understand.

5. The blog post by Adit Deshpande from 2016, titled The 9 Deep Learning Papers You Need To Know About (Understanding CNN’s Part 3) covers nine real-life applications of what you learned in this chapter, and you can then go on to study these examples in more depth. You’ll stumble upon quite a few concepts that will be brand new for you, but, with the knowledge you have now, you’ll be able to manage a general overview of these papers.