This guide describes how to process audio files in Android, in order to feed them into deep learning models built using TensorFlow.
TensorFlow Lite’s launch and subsequent progress have reduced the distance between mobile development and AI. And over time, don’t be surprised if app stores eventually end up flooded with AI/ML-powered apps.
Continue reading A Definitive Guide for Audio Processing in Android with TensorFlow Lite Models
This tutorial guides Android developers in preparing the popular library OpenCV (Open Computer Vision) for use. Through a step-by-step guide, the library will be imported into Android Studio (the official IDE for Android).
Upon installation and setup, OpenCV can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision.
Continue reading 3D Face Reconstruction with Position Map Regression Networks
According to Snapchat’s founders, the smartphone camera has always been at the heart of their app. Over the years, Snapchat has evolved to do much more to your photos than simple image and video messaging.
This giant social media platform is most popular among young people, and the main reason for this is its amazing photo/video editing tools and a wide variety of available camera filters.
Continue reading Creating Snapchat Augmented Reality Lenses in Lens Studio—A Beginner’s Guide
I was inspired by this example of Core ML + ARKit. But I found one significant disadvantage — it doesn’t place annotations on objects automatically. Instead, you need to center the object in your camera view and use your finger to place an annotation.
In my opinion, this destroys user expectations, so I decided to fix that and build a more immersive user experience using object detection in augmented reality.
Continue reading Core ML + ARKit: Annotating Objects in Augmented Reality
One of the most popular languages in the data science and machine learning world is Python, and most Python tasks usually involve at least 5 packages that need to be installed. However, these packages typically have their own dependency packages for them to function well. And some of these dependency packages must be in a specific version. This is where a Python virtual environment comes into play.
Continue reading Creating Python Virtual Environments with Conda: Why and How
It doesn’t matter whether you’re a student, a researcher, a data scientist, a machine learning engineer, or just a machine learning enthusiast, you’re surely going to find PyTorch very useful.
As you might be aware, PyTorch is an open source machine learning library used primarily for applications such as computer vision and natural language processing.
Continue reading 10 reasons why PyTorch is the deep learning framework of the future
Hello seekers! In this post (part 2 of our short series — you can find part 1 here), I’ll explain how to implement an image segmentation model with code. This model will allow us to change the background of any image, just by using the API that we’ll build.
Continue reading Changing Backgrounds with Image Segmentation & Deep Learning: Code Implementation
In this articles series, we’ll aim to build a deep learning library in the mold of TensorFlow and PyTorch, focusing on implementing the same core components. Here’s a preview of some of the concepts and tasks we’ll cover:
The major aim of this project is to drive us away from the comfort zone of using Python, which will help us understand the core principles behind popular deep learning libraries. Doing this in JavaScript can also aid us in implementing the same thing in other programming languages.
Continue reading Create a Deep Learning Library in JavaScript from Scratch (Part 1)
Convolutional Neural Networks (CNN) are frequently preferred in computer vision applications because of their successful results on object recognition and classification tasks. CNNs are composed of many neurons stacked together. Computing convolutions across neurons require a lot of computation, so pooling processes are often used to reduce the size of network layers.
Continue reading Capsule Networks: A new and attractive AI architecture
