An Introduction to Deep Learning

Understand Deep Learning

Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation.

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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. Read More…

Introduction to Machine Learning for Developers

Understanding Machine LearningToday’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…

Introducing the Algorithmia R Client

Algorithmia now has an R Client for developersToday, we’re excited to announce full development support for R on Algorithmia with our new CRAN package.

R users now have access to Algorithmia’s library of more than 2,500 algorithmic microservices via the client.

With the new client, R users can now deploy their predictive models and analytical routines as production-ready API’s without ever having to provision, configure, or manage servers or infrastructure. Read More…

Using R to Build a Sentiment Analysis Forecasting Pipeline

Using R to Forecast Sentiment AnalysisTime 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.

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Understand Customer Data Using Time Series and Sentiment Analysis

Analyzing Sentiment Over TimeWhile 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 and return a sentiment time series plot and frequency. Since the microservice handles most of the data processing via an API call, you can spend more time concentrating on your analysis and less time writing code.  Read More…