Stephanie Kim

Recent Posts

November 28, 2016

An Open Source AWS AMI for Training Style Transfer Models

Create Your Own Style Transfer Model

Warning: The underlying repository isn't working anymore. The training process is broken until further notice. Sorry for the inconvenience.

Second Warning: Don't forget to terminate your EC2 instance after you're done. You still get billed for the instance...

November 22, 2016

How to Retrieve Tweets By Keyword and Identify Named Entities

Last week we introduced the named entity recognition algorithm for extracting and categorizing unstructured text.

In this post we'll show you how to get data from Twitter, clean it with some regex, and then run it through named entity recognition. With the output we get from the algorithm, we...

November 11, 2016

Using Named Entity Recognition to Categorize Text Data

Unstructured text content is rich with information, but it’s not always easy to find what's relevant to you.

With the enormous amount of data that comes from social media, email, blogs, news and academic articles, it becomes increasingly hard to extract, categorize, and learn from that...

November 03, 2016

A Fast Way to Scrape Image URLs from Webpages

Let's say you've created an awesome application that colorizes images. Everybody loves it, but some users are getting errors.

You realize they're trying to pass a URL to a webpage with an image on it, instead of a direct path to the image itself. Your app is expecting a .JPG, or .PNG.

October 31, 2016

Introduction to Machine Learning for Developers

Today’s developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don’t know where to start.

One of the most important aspects of developing smart applications is to understand the underlying machine learning models, even if you...

October 27, 2016

Using R to Build a Sentiment Analysis Forecasting Pipeline

Time series forecasting algorithms are a common method for predicting future values based on historical data using sequential data, such as snowfall per hour (anyone ready for snowboarding season?), customer sign-ups per day, or quarterly sales data. In this R recipe, we'll show how to easily...

October 20, 2016

Understand Customer Data Using Time Series and Sentiment Analysis

While data science offers many ways to visualize and make predictions with your customer data, most can be time consuming. Worse, you often can't reuse your code with other datasets.

Sentiment Time Series is a microservice that can be used on a variety of datasets to process unstructured text...

September 08, 2016

Use LDA to Classify Text Documents

The LDA microservice is a quick and useful implementation of MALLET, a machine learning language toolkit for Java. This topic modeling package automatically finds the relevant topics in unstructured text data.

The Algorithmia implementation makes LDA available as a REST API, and removes the need...

August 09, 2016

Machine Learning Datasets For Data Scientists

Finding a good machine learning dataset is often the biggest hurdle a developer has to cross before starting any data science project. Whether you're new to machine learning, or a professional data scientist, finding a good machine learning dataset is the key to extracting actionable insights....

August 01, 2016

An NLP Approach to Analyzing Twitter, Trump, and Profanity

Who swears more? Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let's find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets.

This walkthrough will provide a basic introduction to help developers of all...

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