Basic Approaches in Object Detection and Classification by Deep Learning

This chapter introduces the basics of object detection and classification as target for deep learning. It concisely covers traditional methods such K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Machines as precursor to deep learning. From there, a description of deep learning in the context of artificial intelligence is presented. This is then followed by a quick look at frameworks for deep learning with references provided for deeper understanding. The aim is to give reader a big picture of the position of deep learning and how it evolved. The scope of this book is given at the end of the chapter. Finally, some self-evaluation exercises are given to emphasize the key takeaways from the chapter. We also provide a list of references for further reading.