Digests » 39

ai

Analyzing suppressed data: Oregon graduation rates by gender, racial and ethnic groups

The Every Student Succeeds Act (ESSA), enacted in 2015, requires states to provide data “that can be cross-tabulated by, at a minimum, each major racial and ethnic group, gender, English proficiency status, and children with or without disabilities,”1 taking care not to reveal personally identifiable information about any individual student.

Machine Learning for Everyone

Machine Learning is like sex in high school. Everyone is talking about it, a few know what to do, and only your teacher is doing it. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairytales about artificial intelligence, data-science magic, and jobs of the future.

Installing and Running Jupyter Notebooks on a Server

Jupyter Notebook is a powerful tool, but how can you use it in all its glory on a server? In this tutorial you will see how to set up Jupyter notebook on a server like Digital Ocean, AWS or most other hosting provider available. Additionally, you will see how to use Jupyter notebooks over SSH tunneling or SSL with with Let’s Encrypt.

Classification with TensorFlow and Dense Neural Networks

I’m going to cover how we can tackle classification with a dense neural network. I’ll be using the same dataset and the same amount of input columns to train the model, but instead of using TensorFlow’s LinearClassifier, I’ll instead be using DNNClassifier. We’ll also compare the two methods.

A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks

Empirical studies show that gradient based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data.