In the first post in this series on Artificial Intelligence: Monster or Mentor? we saw that there are several key ways in which AI advances can improve human productivity in organizations. In this article, we’ll look at the first: Distillation. Distillation is applying AI approaches to automate making large data volumes interpretable. Just like miners distill tons of raw ore into ounces of gold using machines, the goal is to automate the identification of value in big data. Here, we’ll focus specifically on how Distillation can be applied to the business problem of customer experience.
The following problems appeared in the first few assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments. Classifying the letters with notMNIST dataset Let’s first learn about simple data curation practices, and familiarize ourselves with some of the data that are going to be used for deep learning using tensorflow. The notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking a little more like real data: it’s a harder task, and the data is a lot less ‘clean’ than MNIST.
We saw in the previous charts some basic and well-known types of charts that googleVis offers to users. Before continuing with other, more sophisticated charts in the next parts we are going to “dig a little deeper” and see some interesting features of those we already know. Read the examples below to understand the logic of what we are going to do and then test yous skills with the exercise set we prepared for you. Lets begin!
This blog entry concerns our course on “Operations Reserch with R” that we teach as part of our study program. We hope that the materials are of value to lectures and everyone else working in the field of numerical optimiatzion.