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In this post, we’re going to learn about the most basic regressor in machine learning—linear regression. Specifically, we’re going to walk through the basics with a practical example in Python, and learn about applying feature engineering to the dataset we work with.

This is a two-part article, and this part encompasses the basics of feature engineering and regression. In part 2, we’ll discuss gradient descent (coding it from scratch), regularization, and other relevant concepts.

Continue reading Understanding the Mathematics Behind Linear Regression

Heads up! This is an end-to-end series. In its three parts, I’m going to show you how to train, save, and deploy a recommender model. Specifically, you will understand how to get and process your data, build and train a neural network, package it in an application, and finally serve it over the internet for everyone to see and use.

Continue reading Build, Train, and Deploy a Book Recommender System Using Keras, TensorFlow.js, Node.js, and Firebase (Part 1)

Fast and Furious Image Loading (or FFImageLoading) in my opinion—and many others—is a must have in most Xamarin applications. If your application deals with a lot of images, it’s crucial to have this awesome library.

FFImageLoading supports a wide range of Platforms: Xamarin.iOS, Xamarin.Android, Xamarin.Forms, Xamarin.Mac / Xamarin.Tizen, and Windows (UWP, WinRT).

Continue reading Using FFImageLoading in Xamarin.Forms for caching and optimizing images

In my last blog on using the Room database in Android, we took a look at how to save/fetch our user data to/from a SQL database without having to write a lot of boilerplate code. If you haven’t read it yet, I would recommend that you do so before you go ahead with this one:

Continue reading Using LiveData with Room in Android

In this article we’ll be looking at end-to-end ML platforms. And while most of these platforms offer robust tools for managing ML pipelines from model training to deployment, and beyond, it’s important to note that data collection and labeling is still left to the tools we covered in part one.

That’s certainly not to take anything away from the incredible platforms we’ll look at here, but it is something important to remember when thinking about implementing ML solutions in your organization.

Continue reading “Best End-to-End ML Platforms for 2024”

There’s a joke going around the dev community about serverless tech stacks using servers! Some even take this in an offensive way. But what’s missing from this joke are the advantages serverless computing has to offer.

Reduced development time and cost-effective operations are a couple of factors that we should be looking for, and Amplify as a serverless solution offers you that. Spending time and energy writing and wiring your application is worth more than continuously managing resources and worrying about them when it’s time to scale. Serverless is a pragmatic solution for many use cases in web and mobile development.

Continue reading Building a React Native Mobile App with AWS Amplify and Expo

If your in-house mobile machine learning model gives you an advantage over your competitors, you’ll probably want to keep it private. Unfortunately, at the moment, Apple doesn’t offer any approaches to keep Core ML models private.

Anyone can get your .mlmodel file (or the compiled version mlmodelc) and reuse it in third party applications. In this article, you’ll learn a few strategies for protecting your Core ML models.

Continue reading Protecting Core ML Models

Are you looking for fast deep learning modeling? If so, Keras is going to be your natural choice. But there are so many deep learning frameworks available today, and the list is growing very fast—why choose Keras?

Keras has the capability and resources to run on top of popular deep learning libraries like TensorFlow, Theano, or CNTK. It also offers a relatively simple API that manages to also offer a lot of flexibility. This makes Keras easy to learn and easy to use. Isn’t that enough reason to start using Keras?

Continue reading Linear Regression using Keras and Python

On the Snapchat Lens Explore page, hundreds of thousands of lenses are listed each day. Different than your carousel, on the Explore tab users can search and discover new lenses based off of keywords, the people they follow, and their recommended interests.

Every few months, some of Snapchat’s Lens Creators — official and not — will shift their designs to meet a new trend. This trend is usually reflected through popular TikTok videos, Instagram’s trending aesthetics, or a pop culture event. Currently, following the rise of sentimentality for the early 2000s, trends like maximalism and cyber-core have resurfaced, tackling the combination of old-school internet and the new wave of abstract-yet-useable Lens concepts to make some pretty interesting combinations.

Continue reading Lens Trend Spotlight: “Cyber Core”

LiDAR is a sensor currently changing the world. It is integrated in self-driving vehicles, autonomous drones, robots, satellites, rockets, and many more.

This sensor understands the world in 3D using laser beams and measuring the time it takes for the laser to come back. The output is a point cloud.

It’s a technology harder to understand than the camera because less people have access to it. I had access to it and today will help you understand how LiDAR detection works for self-driving cars.

Continue reading How LiDAR Object Detection Works