We just accomplished our second computer vision project in this R and deep learning journey! Through this chapter, we got more familiar with convolutional neural networks and their implementation in MXNet, and another powerful deep learning tool: Keras with TensorFlow.
We started with what self-driving cars are and how deep learning techniques are making self-driving cars feasible and more reliable. We also discussed how deep learning stands out and becomes the state-of-the-art solution for object recognition in intelligent vehicles. After exploring the traffic sign dataset, we developed our first CNN model using MXNet and achieved more than 99% accuracy. Then we moved on to another powerful deep learning framework, Keras + TensorFlow, and obtained comparable results.
We introduced the dropout technique to reduce overfitting. We also learned how to deal with lack of training...
