Blog

Deep learning has seen tremendous progress over the past few years. This is largely due to the emergence of deep learning frameworks such as PyTorch and TensorFlow, which have greatly simplified even the most sophisticated research.

With smartphones having become the devices we use the most, the next wave of innovation is going to center on how we can leverage these rapid advances in deep learning to enhance our smartphone experiences. AI-powered mobile applications will become (and already are) smart enough to understand us and help us perform tasks via visual perception, language understanding, and voice recognition, even when not connected to the internet.

Continue reading PyTorch Mobile: Image classification on Android

In a previous article, I discussed the possibilities of computer vision-based deep learning with both RNNs and CNNs.

Generally, ML engineers will specialize in one model architecture and let the other slide.

My point and purpose for writing this post is the following: learning both allows to tackle a wider range of use-cases.

Continue reading Recurrent Neural Networks (RNNs) in Computer Vision: Image Captioning

Linear regression is an algorithm that finds a linear relationship between a dependent variable and one or more independent variables. The dependent variable is also called label and independent variables are called features as well.

Linear regression is one of the most foundational algorithms for statistical and machine learning analysis.

PyTorch is an open source machine learning framework introduced by Facebook in 2016. PyTorch is based on the Torch library, and it’s a Python-based framework as well. Apart from its Python interface, PyTorch also has a C++ front end.

Continue reading Linear Regression with PyTorch and Python

Matplotlib is the most popular data visualization library in Python. It allows us to create figures and plots, and makes it very easy to produce static raster or vector files without the need for any GUIs.

This tutorial is intended to help you get up-and-running with Matplotlib quickly. We’ll go over how to create the most commonly used plots, and discuss when to use each one.

Continue reading Introduction to Matplotlib — Data Visualization in Python

The quality of images is relevant in building compression and image enhancement algorithms. Image Quality Assessment (IQA) is divided into two main areas; reference-based evaluation and no-reference evaluation.

Reference-based methods rely on high-quality images to evaluate the difference between two images. Structural Similarity Index is one example of a reference-based method. Unlike reference-based methods, however, no-reference methods don’t require a base image for evaluating the quality of an image. These methods just receive an image whose quality is being assessed.

Continue reading Research Guide: Image Quality Assessment for Deep Learning

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