Algorithmia Blog - Deploying AI at scale

Investigating User Experience with Natural Language Analysis

User experience and customer support are integral to every company’s success. But it’s not easy to understand what users are thinking or how they are feeling, even when you read every single user message that comes in through feedback forms or customer support software. With Natural Language Processing and Machine Learning techniques it becomes somewhat easier to understand trends in user sentiment, main topics discussed, and detect anomalies in user message data.

A couple of weeks ago, we gave a talk about investigating user experience with natural language analysis at Sentiment Symposium and thought we’d share the talk, along with the speaker notes for anyone who is interested.

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Introduction to Reinforcement Learning

We only understand a sliver of how the brain works, but we do know that it often learns through trial and error. We’re rewarded when we do good things and punished when we do the wrong ones; that’s how we figure out how to live. Reinforcement Learning puts computational power behind that exact process and lets us model it with software.

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Algorithmia at Collision’s Growth Summit

Our CEO Diego spoke last week at Collision down in New Orleans. The panel with NODE‘s Falon Fatemi, Portworx’s Murli Thirumale and Tom Komkov covered topics ranging from the pressures inherent in growth financing to what the panelists would do differently over the past 12 months. Share and let us know what you think!

Introduction to Optimizers

Source: Deep Ideas

If you remember anything from Calculus (not a trivial feat), it might have something to do with optimization. Finding the best numerical solution to a given problem is an important part of many branches in mathematics, and Machine Learning is no exception. Optimizers, combined with their cousin the Loss Function, are the key pieces that enable Machine Learning to work for your data.

This post will walk you through the optimization process in Machine Learning, how loss functions fit into the equation (no pun intended), and some popular approaches. We’ll also include some resources for further reading and experimentation.

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Introduction to Loss Functions

The loss function is the bread and butter of modern Machine Learning; it takes your algorithm from theoretical to practical and transforms neural networks from glorified matrix multiplication into Deep Learning.

This post will explain the role of loss functions and how they work, while surveying a few of the most popular of the past decade.

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