As 2018 comes to a close, we’d like to take a look back to see how our readers have interacted with our blog, which articles were the most read, and what that could tell us about the field of machine learning writ large.
We know 2019 will be a year of tremendous progress in tech, and we’re relentlessly curious and eager for it. We look forward to adding more algorithms for our marketplace, expanding our AI Layer to more industries, producing interesting articles about novel tech applications, and engaging with innovators in the AI and machine learning fields.
Let’s take a look back on our year:
In March, we published Introduction to Machine Learning to give readers an in-depth look at what machine learning is at the macro and micro level. We got great engagement from this piece and know it will have staying power even as the world of AI morphs and grows.
Machine learning applications in sentiment analysis are becoming more and more popular, and conducting sentiment analysis can provide a company with continuous focus group feedback to gauge customer satisfaction and contentment. The explanation of a specific data use case in How to Perform Sentiment Analysis with Twitter Data was our ninth most read article of 2018.
A post from April on how computer vision works was insanely popular this year. Introduction to Computer Vision was shared more than 4,000 times by our readers, and provides a big-picture overview of the field of machine learning concerned with training computers to identify elements in images. It’s a hot topic in AI because of the pervasiveness of this technology. As our CEO said last year,
Used to be if the product was free you were the product , now if a product is free you are the training set.
— Diego Oppenheimer (@doppenhe) October 6, 2017
Introduction to Emotion Recognition was another tech overview article that was of much interest to curious tech readers in 2018. Like computer vision, emotion recognition trains computers to read the facial expressions of people in images to decipher their moods. This technology has many possible applications, including criminal justice: polygraph analysis, juror psychology, security surveillance systems and interrogation tactics, or in industry for fatigue monitoring for pilots and drivers.
Haven’t you always wanted to know how deep learning works without ground truth? Introduction to Unsupervised Learning is for you (and for the more than 7,500 other avid AI news consumers who have read this post since April. And no, before you ask, unsupervised learning is not about classrooms without teachers present; actually it kind of is.
Our intro posts sure were popular this year! (Perhaps in 2019 we’ll move on to intermediate posts.) Introduction to Optimizers comes in at the number four most-read article. Optimizers shape and mold machine learning models into their most accurate possible forms, and they’re the cousin of loss functions (see below).
Much is still unfolding in the machine learning software field; but hardware is just as important when running multivariate algorithms at scale. Learning the different compute modes and which is best for building and deploying ML applications was a topic of supreme interest for nearly 12,000 savvy readers out there this year. Make some time today to read Hardware for Machine Learning.
Facial recognition software was in the news a lot in 2018 so it makes sense that our post, Facial Recognition Through OpenFace was so popular. This article gives a good technical run-down of how OpenFace, a facial recognition machine learning model works.
Remember optimizers from above? Loss Functions can also evaluate machine learning models by determining how well an algorithm is modeling a dataset. Learn more about this tool in Introduction to Loss Functions, which helped educate more than 17,000 people this year.
And finally! Our number one most-read post of 2018 is Convolutional Neural Networks in PyTorch! Convolutional neural networks are algorithms that work in tandem on large projects #convoluted (typically computer vision). Check out this deep dive into the Python-based framework, PyTorch, and how it easily enables development of machine learning work flows.