Build your own neural network classifier in R by Jun Ma.
From the post:
Image classification is one important field in Computer Vision, not only because so many applications are associated with it, but also a lot of Computer Vision problems can be effectively reduced to image classification. The state of art tool in image classification is Convolutional Neural Network (CNN). In this article, I am going to write a simple Neural Network with 2 layers (fully connected). First, I will train it to classify a set of 4-class 2D data and visualize the decision boundary. Second, I am going to train my NN with the famous MNIST data (you can download it here: https://www.kaggle.com/c/digit-recognizer/download/train.csv) and see its performance. The first part is inspired by CS 231n course offered by Stanford: http://cs231n.github.io/, which is taught in Python.
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One suggestion, based on some unrelated reading, don’t copy-n-paste the code.
Key in the code so you will get accustomed to your typical typing mistakes, which are no doubt different from mine!
Plus you will develop muscle memory in your fingers and code will either “look right” or not.
Enjoy!
PS: For R, Jun’s blog looks like one you need to start following!