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Following what we learned about human vision, we’re now going to transfer some of that knowledge to computer vision (exciting right?!).

Very quickly, to quickly explain/recap this series, Joseph Redmon released a set of 20 lectures on computer vision in September 2018. As he’s an expert in the field, I wrote a lot of notes while going through his lectures. I am tidying my notes for my own future reference but am posting them on Medium also, to hopefully be useful for others.

Continue reading “The Ancient Secrets of Computer Vision 3 by Joseph Redmon — Condensed”

Using ML Kit, the language of a string of text can be determined. The ML Kit API supports over 100 languages. It can also identify native and romanized text from languages such as Russian and Arabic. Given a string of text, the API provides the most likely languages as well as the confidence level. Let’s look at how that can be done.

Continue reading “Identify the language of text with ML Kit on Android”

Phased Long Short Term Memory is an improvement on the well known Long Short Term Memory units. Its main advantage is its ability to deal with data that do not follow a simple sequence and data with long timesteps.

Phased LSTM differs from LSTM by the possession of an additional gate called the time gate. The phased version is very efficient and performs better than basic LSTMs, even when given fewer data.

Continue reading “Phased Long Short Term Memory”

For the last few months, I’ve been working on my own machine learning model to predict what kind of coffee (hot or iced) I should drink on any given day, based on a variety of factors.

I’ve become very interested in machine learning over the past year, and so I wanted to build something to help me learn—so I built an app (or 2)!

Continue reading “CoffeeBot — Using Scikit-learn, Core ML for iOS, and Alexa to predict the right coffee to drink”

Many organizations are leveraging machine learning to analyze large customer databases and identify customer loyalty; or, perhaps more importantly, which customers are at the highest risk of churning.

Accurate prediction of churn is extremely valuable and, if the right steps are taken to retain customers at risk, businesses can lift LTV across an entire portfolio.

Continue reading “Machine Learning Techniques for Predicting Customer Loyalty”

When developing in any programming language or framework, it’s usually a good idea to learn and follow established best practices. This is even more crucial in Flutter given the way widgets are built and re-built.

Following established best practices in Flutter is also particularly important for a couple of specific reasons: code readability and application performance.

In this post, we’ll cover some of the best practices in Flutter that can help on both of these fronts.

Continue reading “Flutter Development Best Practices”

Snapchat: A pioneer in mobile machine learning

Whenever someone asks me to explain what mobile machine learning is, I instinctively bring up Snapchat as a core example.

In 2015, the incredibly popular social content platform added Lenses to their mobile app—if you’ve ever played with Snapchat, you know these well. They’re essentially augmented reality (AR) filters that give you big strange teeth, turn your face into an alpaca, or trigger digital brand-based experiences.

Continue reading “Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat”

SwiftUI is defined as being declarative and reactive. The former is what allows us to write out our UI, which we can do in a very clean and organized fashion. The latter is what brings our UI and data closer together than ever.

We’ve been working on an app that allows us to view our team and dive into the profile pages of each member.

Continue reading “SwiftUI: Observables, View Hierarchy, and Putting Them All Together”

In order for data science teams to outsource annotation to a managed workforce provider — also known as a Business Process Outsourcer (BPO) — they must first have the tools and infrastructure to store and manage their training data.

Data management tools and infrastructure should support R&D product management teams, outsourced labeling teams, and internal labeling and review teams, working together in a single centralized place with fully transparent oversight.

Continue reading “How to Scale Training Data – Complete Guide”