We started this series with the core intention of designing cross-platform apps that can provide the same performance as native Android and iOS apps. …

In the previous post of my Beginner’s Guide to NativeScript series, we kicked things off by focusing on the high-level benefits of using NativeScript, along with a simple demo app to get us started. If you haven’t read this introduction, and you’re unfamiliar with NativeScript, I’d suggest taking a look: Beginner’s Guide to NativeScript: Creating Your First Cross-Platform App Building cross-platform mobile experiences using NativeScriptheartbeat.comet.ml

Last month, I was asked to create Android and iOS versions of an app for a product. And here’s what happened… Being a JavaScript developer, I always find it difficult to design Android and iOS apps to provide a native experience to the users. I asked my friend—a great Android…

This is the final post in our series covering the ins and outs of working with Google’s AutoML Vision Edge platform. We started our journey by training a simple machine learning model in 3–4 hours. From there, we exported the model into various formats provided by AutoML. …

This is the fifth blog post in my AutoML Vision Edge series. The post will help you run AutoML Edge models on Docker containers. If you haven’t been following this blog post series till now, here’s a quick recap: We started the series by learning how we can train and…

Running TensorFlow.js models on a Node.js server — This post is the 4th post in my series on Google Cloud’s AutoML Vision Edge. The post will cover using the Tensorflow.js models on NodeJS servers. If you are not following the AutoML Vision Edge series till now, here is some recap: We started the series by learning how we…

This is the third post in the Google Cloud AutoML Vision Edge Series. In the first post, we trained and used a .tflite format of the AutoML model. In the second post, we used the .pb file or the TF Saved Model format provided by AutoML using Python. As we’ve…

This post is part 2 in the series on Google Cloud AutoML Vision Edge. In the previous post, we saw how we can train an edge-ready TensorFlow Lite model with AutoML, from scratch. …

Though I support learning machine learning from scratch, I’m always interested in validating the classic Cost + Effort < Output. Machine learning is interesting conceptually, but at the same time, it’s hard to understand for a beginner. A lot of effort is needed to master and understand the foundational mathematics…