Whether you’re a scientist analyzing earthquake data to predict the next “big one”, or are in healthcare analyzing patient wait times to better staff your ER, understanding time series data is crucial to making better, data informed decisions.
This gentle introduction to time series will help you understand the components that make up a series such as trend, noise, and seasonality. It will also cover how to remove some of these components and give you an understanding on why you would want to. Some common statistical and machine learning models for forecasting and anomaly detection will be explained and we’ll briefly dive into how neural networks can provide better results for some types of analysis. Read More…
Facial recognition has become an increasingly common technology.
Today smartphones use facial recognition for access control, and animated movies use facial recognition software to bring realistic human movement and expression to life. Police surveillance cameras use it to identify people who have warrants out for their arrest, and it is also being used in retail stores for targeted marketing campaigns. And of course, celebrity look-a-like apps and Facebook’s auto tagger also uses facial recognition to tag faces.
Not all facial recognition libraries are equal in accuracy and performance, and most state-of-the-art systems are proprietary black boxes.
OpenFace is an open-source library that rivals the performance and accuracy of proprietary models. This project was created with mobile performance in mind, so let’s look at some of the internals that make this library fast and accurate, and work through some use cases on why you might want to implement it in your projects. Read More…
Amazon reports that there are now “tens of millions” of Alexa-enabled devices in use, from the compact Echo Dot to the revamped Alexa-enabled Fire Stick and Kindle. Voice-enabled devices are hotter than ever, but would be nearly useless without the wide variety of external services they rely on. Whether you’re asking Alexa to turn on the lights or tell you the weather, there’s a microservice in the loop, responding intelligently to your requests.
As a developer, how do you bring your own algorithm or service into Alexa? If your code is relatively simple Node, Python, Java, or C#, then you can use AWS Lambda for your base logic. If you’re using other languages, complex frameworks, or big GPU-dependant Machine Learning models, you may want to consider Algorithmia. Even if your core functionality is not complex, Algorithmia’s library of 4500+ ready-to-run algorithms can superpower your Alexa app, quickly adding advanced NLP, web scraping, image processing, and other turnkey machine-learning tools. Read More…
A good image editor has a wide variety of features, from simple resizing to advanced photo manipulation. A good software platform needs similar tools as well, and when run in a scalable serverless environment, can include a variety of powerful image-transformation and data-extraction algorithms fueled by machine learning.
We’ve been building up a library of image-related algorithms for some time, created both by our in-house staff and our amazing community of 60,000 developers. If you’re interested in building algorithms and making them available to the community (as open-source or for royalty payments), it’s easy to publish an algorithm on Algorithmia!
Meanwhile, check out these great tools which you can use from any programming language, allowing you to code up complex image-editing and image-analysis workflows with just a few lines of code… Read More…
Apache Spark is one of the most useful tools for large scale data processing. It allows for data ingestion, aggregation, analysis and more on massive amounts of data and has been widely adopted by data engineers and other professionals.
With Spark Streaming and Spark SQL you can perform ETL operations in real-time on data coming from a variety of sources such as Kafka or Flume. And now if you want to do some basic machine learning, you can do that with SparkML, which is a library where they bring core statistical models like KMeans or decision tree models to users in a high level API.
But what if you want to analyze thousands of Tweets in real time, yet you don’t have a trained dataset to discover the sentiment of those tweets. Or maybe you want to classify documents on the fly or remove profanity from text or nudity from images?
Algorithmia’s over 4,000 pre-trained models and functions cover all of the above use cases and perfectly compliment Spark’s core functionality. These pre-trained models can easily integrate into Spark via a REST API endpoint. And just like Spark, Algorithmia has Python, R, Java, and Scala clients so you can stay in the language you’re familiar with while building robust machine and deep learning pipelines that scale with your data.