We kick off our R deep learning journey with the fundamental and core concepts of deep learning, and a deep learning 101 project—handwritten digit recognition. We will start with what deep learning is about, why we need it, and its evolution in recent years. We will also discuss why deep learning stands out and several typical deep learning applications. With the important deep learning concepts in mind, we get it started with our image classification project where we first conduct exploratory analysis on the data and make an initial attempt using shallow single-layer neural networks. Then we move on with deeper neural networks and achieve better results. However, we argue that chaining more hidden layers does not necessarily improve classification performance. The key is to extract richer representation and more informative features. And convolutional neural networks (CNNs) are the way to go! We will be demonstrating how we boost the digit recognition accuracy to nearly 99% with CNNs, which are well suited to exploiting strong and unique features that differentiate between images. We finally wrap up the chapter after several more experiments and validations.

We will look into these topics in detail: