Once upon a time, site mappers were arcane scripts which could take hours or days to examine a single website. But, thanks to scalable & interoperable cloud algorithms, it now takes only minutes… and includes a multitude of handy features powered by machine learning: auto-tagging, summarization, page-ranking, and more!
- GetLinks recursively traverses a website of your choice, plotting them on a force-directed graph via D3
- PageRank examines the pages to create an ordered list akin to Google’s PageRank Algorithm
- Url2Text grabs the text from each page, allowing Summarizer to extract topic sentences while AutoTag generates keywords
Learn more in our Introduction to Microservices article.
Algorithmia was delighted to speak at Seattle’s Building Intelligent Applications meetup last month. We provided attendees with an introductory view of machine learning, walked through a bit of sample code, touched on deep learning, and talked about about various tools for training and deploying models.
For those who were able to attend, we wanted to send out a big “thank you!” for being a great audience. For those who weren’t able to make it, you can find our slides and notes below, and we hope to see you at the next meetup on Wednesday, April 26. Data Scientists Emre Ozdemir and Stephanie Peña will be presenting two Python-based recommender systems at Galvanize in Pioneer Square.
To come to Wednesday’s talk, RSVP via Eventbrite. To keep an eye out for future events, join the Building Intelligent Applications Meetup Group.
You may already know that Algorithmia hosts scalable deep learning models. If you are a developer, you’ve seen how easy it is to run over 3,000 microservices through any of our supported languages and frameworks.
But sometimes it’s nice just to play with a simple demo.
The Deep Fashion microservice is a deep CNN, performing multi-category classification, which has been trained with humans in the loop to recognize dozens of different articles of clothing. It can be used standalone to locate specific items in an image set, or combined with a nearest-neighbors service such as KNN or Annoy to recommend similar items to online shoppers. And since the service provides bounding box coordinates for each item within the image, it could even used to censor or modify images themselves.
To see it in action, just head over to the Deep Fashion Demo, click (or upload) an image, and watch as state-of-the-art deep learning models scan the image to identify clothing and fashion items.
When we look at an image, it’s fairly easy to detect the horizon line.
For computers, this task is somewhat more difficult: they need to understand the basic structure of the image, locate edges which might indicate a horizon, and pare out the edges which do not matter. Fortunately, Algorithmia boils this all down to a single API call: just send your image to deep horizon, an algorithm for horizon detection, and it tells you where the horizon line is.
If you read our recent post on language detection, you already know how easy it is to use Algorithmia’s services to identify which language a given piece of text is written in.
Now let’s put that into action to perform a specific task: organizing documents into language-specific folders.
We’ll build our Python language detection microservice using Algorithmia’s language identification algorithm. Then, we’ll look through all the .txt and .docx files in a directory to see which language each one is written in.