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Reinforcement learning is a type of machine learning in which an agent learns to behave in an unknown environment by performing actions and seeing the ensuing results. The agent’s objective is to learn to act in ways that maximizes expected long-term rewards.

When it comes to reinforcement learning, there is no expected outcome—the agent makes the best decision based on its knowledge, for which it is either rewarded or penalized. As such, it is bound to learn from past experiences.

Continue reading “Introduction to Reinforcement Learning”

We’ve talked with more than 20 amazingly talented Snapchat Lens Creators over the last few months, and while they’ve been unanimously excited about the potential of creating Lenses with custom machine learning models, there remains an recurring caveat attached to the end of that sentiment—when I can get a grasp of the ML side of things.

Machine learning is tough—especially when you don’t have much in the way of knowledge or previous experience. And the same thing (so far) goes for working with ML in Lens Studio.

Continue reading “Machine Learning Primer for Snapchat Lens Creators”

In a normal machine learning problem, we get an observation for which we predict an outcome, irrespective of the time factor. In some of these cases, future outcomes are being predicted, but that treats all the past observations equally, with little or no significant difference.

However, a time series dataset is entirely different. Time series tasks add a “time dimension”, and also have an explicit order of dependence between the observations. To put it simply: A time series is a sequence of observations taken sequentially in time.

Continue reading “Time Series Forecasting in Machine Learning”

If you’ll recall, during Google I/O 2018, Google announced the WorkManager library as part of the Android Jetpack component set. It basically replaced JobScheduler as Google’s recommended way to enqueue background tasks that are guaranteed to execute.

Continue reading “WorkManager in Android: Enqueue and execute background tasks”

At the heart of any machine learning initiative is data—it’s required to train models and serves as the base on which models are applied.

It follows, then, that to effectively train and implement machine learning models, you must have good data. Unfortunately, curating high-quality data often isn’t an easy task.

This article explains how open source dataset initiatives contribute to the development of machine learning models. You’ll also learn about popular open source dataset initiatives for machine learning, and discover what challenges to expect when using publicly-available data.

Continue reading “Open Source Datasets for Machine Learning: Challenges and Solutions”

If I gave you 30 minutes and a reasonably powerful computer, you could train a machine learning model to recognize dozens of breeds of dogs with higher accuracy than most humans.

This is partially due to great tools and frameworks like TensorFlow or PyTorch, but it’s also due to the availability of data. At some point, generous researchers decided to take the time to label a few thousand images of pets and release that data publicly.

Try to train a similar model for species of fish, and you’ll find yourself spending weeks or thousands of dollars sourcing and labeling data yourself.

Continue reading “Synthetic Data: A bridge over the data moat”