In our introduction to saliency detection post, we showed how to harness the power of the human brain using a saliency algorithm to detect the most distinct and noticeable objects in an image.
In an earlier post, we introduced the Sentiment Analysis algorithm and showed how easy it was to retrieve the sentiment score from text content through an API call.
In this post, we’ll show how to build a sentiment analysis pipeline that grabs all the links from a web page, extracts the text content from each URL, and then returns the sentiment of each page.
Reading emotional expression is one of the most difficult tasks for humans, let alone computers. Two people looking at the same photo might not agree whether someone is grimacing or grinning. Until recently, computers weren’t much better at the job, either.
Fortunately advances in deep learning has brought us speed, efficiency and accuracy in detecting people’s emotions in photos.
Create Your Own Style Transfer Model
In keeping with our mission to democratize access to state of the art algorithms, we’re pleased to open source the AWS AMI needed to launch an EC2 P2 instance using GPUs, and the pipeline required to train your own style transfer models.
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 can then group the data by the category each named entity is assigned to, and then extract the categories we are interested in.
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 information.
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. Read More…
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 aren’t the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning. Read More…
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 link algorithms together to create a data analysis pipeline for sentiment time series forecasting.