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:

- What is deep learning and what is special about it
- Applications of deep learning
- Exploratory analysis on MNIST handwritten digit data
- Handwritten digit recognition using logistic regression and single-layer neural networks with the
`nnet`package - Handwritten digit recognition using deep neural networks with the
`MXNet`package - Rectified linear unit
- The mechanics and structure of convolutional neural networks
- Handwritten digit recognition using convolutional neural networks with the
`MXNet`package - Visualization of outputs of convolutional layers
- Early stopping in deep neural networks