All posts in A.I. Topic Guides

Advanced grammar and Natural Language Processing with Syntaxnet

Parsey McParseface

Lets play a game: can you tell the difference between these two sentences?

“Most of the time, travellers worry about their luggage.”

“Most of the time travellers worry about their luggage.”

Whoa, remove the comma and all of a sudden we’re having an entirely different conversation!

The little nuances of language can be hard enough for a human to understand, let alone a computer! How could we possibly teach a computer to understand the difference?

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Advanced Algorithm Design

We host more than 4000 algorithms for over 50k developers. Here is a list of best practices we’ve identified for designing advanced algorithms. We hope this can help you and your team. Read More…

Making Algorithms Discoverable and Composable

Just like a music producer creates a beat, then combines it with instrumentals and a baseline to form something catchy that lyrics can be applied to… developers need a way to compose algorithms together in a clean and elegant way.

Whether you’re creating a sentiment analysis pipeline for your social data or doing image processing on thousands of photos, you’ll need an easy way to combine the various tools available so you aren’t writing spaghetti code.

It isn’t always easy to combine the libraries you need. Sometimes a library or machine learning model is written in a different language than the one you’re using. Other times there might simply be a performance difference between languages which (is why we chose Rust to create a Video Metadata Extraction pipeline). And even though GitHub offers thousands of libraries, frameworks, and models to choose from, it’s sometimes difficult to find the one you need to solve your problem.

To solve these problems — and allow you to write elegant code while using machine learning models — Algorithmia provides an easy way to find, combine, and reuse models regardless of language. Each one gets a RES API endpoint, so you can mix & match them with each other and with external code. Read More…

Introduction to Computer Vision

Why you need computer visionComputer vision is behind some of the most interesting recent advances in technology. From algorithms that can identify skin cancer as well as dermatologists to cars that drive themselves, it’s computer vision algorithms that are behind these advances.

While CV algorithms have been around in various forms since the 1960s, it wasn’t until recently that it’s progressed to far more sophisticated levels. In particular, combining computer vision with machine learning has yielded some amazing results. Read More…

Introduction to Sentiment Analysis

Algorithmia's Guide to Sentiment Analysis Algorithms

The best businesses understand sentiment of their customers – what people are saying, how they’re saying it, and what they mean. Sentiment Analysis is the domain of understanding these emotions with software, and it’s a must-understand for developers and business leaders in a modern workplace.

As with many other fields, advances in Deep Learning have brought Sentiment Analysis into the foreground of cutting-edge algorithms. Today we use natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories.

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