R for Deep Learning (I): Build Fully Connected Neural Network from Scratch R-bloggers

I would like to thank Feiwen, Neil and all other technical reviewers and readers for their informative comments and suggestions in this post. Deep Neural Network (DNN) has made a great progress in recent years in image recognition, natural language processing and automatic driving fields, such as Picture.1 shown from 2012 to 2015 DNN improved IMAGNET's accuracy from 80% to 95%, which really beats traditional computer vision (CV) methods. In this post, we will focus on fully connected neural networks which are commonly called DNN in data science. The biggest advantage of DNN is to extract and learn features automatically by deep layers architecture, especially for these complex and high-dimensional data that feature engineers can't capture easily, examples in Kaggle. Therefore, DNN is also very attractive to data scientists and there are lots of successful cases as well in classification, time series, and recommendation system, such as Nick's post and credit scoring by DNN.