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Pruning is a technique in deep learning that aids in the development of smaller and more efficient neural networks. It’s a model optimization technique that involves eliminating unnecessary values in the weight tensor. This results in compressed neural networks that run faster, reducing the computational cost involved in training the networks. This is even more crucial when deploying models to mobile phones or other edge devices. In this guide, we’ll look at some of the research papers in the field of pruning neural networks.

Continue reading Research Guide: Pruning Techniques for Neural Networks

Firebase is a Backend as a Service (BaaS) that provides a number of advantages React Native developers building mobile applications. As a React Native developer, if you use Firebase, you can start building an MVP (minimum viable product) to complete mobile applications, keeping your costs low and prototyping the application pretty quickly, without having to set up a complete backend solution.

Continue reading How to build an Email Authentication app with Firebase, Firestore and React Native

Machine learning is moving to the edge. You’ve probably read or heard this sentiment somewhere. Whether it was a WWDC session, an ARM whitepaper, or any number of insightful pieces about the future of ML, the concept of on-device machine learning is expanding in both theoretical and practical terms.

Continue reading Machine learning on mobile: What can you actually do with it?

In this article, we’re going to build a simple sentiment analysis platform using Flask, a lightweight web application framework. Our platform will be able to classify a movie review as either positive or negative. We’ll use the IMDB dataset to build a simple sentiment analysis model, save it, and host it on Heroku. We’ll use Gunicorn to serve our model.

Continue reading Build, Save, and Host Your First Machine Learning Model Using Flask and Heroku

We’ve all heard the saying A picture is worth a thousand words. But is a tarnished image with gaping holes or splotches or blurs worth a few hundred? What if you just found an age-old photograph of your grandparents’ wedding, but the surface was so worn that you could barely make out their faces. Or perhaps you got photobombed in what would otherwise have been the perfect picture. Or maybe you’re like me and are wondering why hasn’t anyone integrated an option in a smartphone camera app to remove unwanted objects from images?

Continue reading Guide to Image Inpainting: Using machine learning to edit and correct defects in photos

🙀 Jetson Wuh? What Is it?

The NVIDIA Jetson Nano is, essentially, an amazing little computer. I’d describe it as a lot like a Raspberry Pi, except it has a GPU.

Combine that with its low $99 USD price tag, and AI/ML enthusiasts see a cheap device that can go from desktop to edge with perfect parity.

An ideal machine for AI in the wild!

Continue reading “Getting Started with the Jetson Nano”

Just recently, the 5th stable version of the React Navigation library was released. Without a doubt, it’s one of the most popular navigation solutions in React Native apps that also has support for Expo.

Recently, it underwent some core changes. Specifically, how you used to define routes up until react-navigation version 4.x.x has some major changes.

Continue reading Getting Started with Stack Navigator Using react-navigation 5 in React Native and Expo Apps

Everyone knows that Swift is an open-source language from Apple and is available on macOS machines. Some of you might even know that Swift can be compiled on iPadOS.

But did you know that Swift is also available on Ubuntu? If yes, have you ever tried it?

Let’s try this out here—this post will walk you through compiling a Swift file on Ubuntu.

Continue reading Getting Started with Swift on Ubuntu

In this tutorial, we’re going to talk about a type of unsupervised learning model known as Boltzmann machines. We assume the reader is well-versed in machine learning and deep learning. We’ll use PyTorch to build a simple model using restricted Boltzmann machines. This model will predict whether or not a user will like a movie.

A Boltzmann machine defines a probability distribution over binary-valued patterns. What makes Boltzmann machine models different from other deep learning models is that they’re undirected and don’t have an output layer. The other key difference is that all the hidden and visible nodes are all connected with each other. Due to this interconnection, Boltzmann machines can generate data on their own. As such, it can be classified as a generative deep learning model.

Continue reading Introduction to Restricted Boltzmann Machines Using PyTorch

But there was one major issue with Prisma. It had to be run through JVM and had memory issues. The Prisma Framework is rewritten in Rust, and it addresses the issues from Prisma 1.

Prisma 1 also required a server in front of your backend server, while with the Prisma Framework, the query engine is now a bundled executable that’s run alongside the backend on the same server.

Continue reading Getting Started with the Prisma Framework (formerly Prisma 2) in React Native