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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”

Introduction

In this post, I’ll explore how I made a Lens that features an animated phoenix flying through an AR scene anchored to the ground. This is the finished Lens:

To get there, we will leverage the Refinement Pack and Particles template from Lens Studio, as well as a 3D phoenix asset from Sketchfab.

Specifically, this post will cover:

Moving an object along a 3D path through the scene using a Prefab from the Refinement Pack Applying a texture to a 3D model of a phoenix downloaded from Sketchfab Working with particle emitters to add a stationary column of smoke, embers throughout the scene, and a bokeh effect that follows the phoenix

Let’s dive in!

Continue reading “Creating an Animated 3D Phoenix Lens in Lens Studio”

Before we begin, let’s get started by answering the following question:

What is a type?

The answer to this question is fairly simple—a type is a collection of related data that encapsulates certain concepts and principles. A type can be abstract or concrete, it defines a set of valid operations that we can perform on it, and finally, a type clearly demarcates the internal and external.

Continue reading “Equality, Identity, and Hashing with Swift Types”