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
Lately, machines have been proving themselves as good as or even superior to people with certain tasks: they are already better in Go, Chess, and even Dota 2. Algorithms compose music and write poetry. Scientists and entrepreneurs estimate that artificial intelligence will greatly surpass human beings in the future.
Continue reading Can artificial intelligence be taught how to joke?
In our last tutorial, we used Firebase to store and keep track of the tasks we needed to complete. But that approach wasn’t customized to a specific user, i.e. they all got the same list of tasks. One way of personalizing these tasks for different users is to include user authentication. And that’s exactly what we’ll be doing in this post.
In today’s digitally transforming world, human beings are encountering more advanced technology on a daily basis. Image recognition software and solutions are becoming a common part of our landscape. The chances are you’ve already interacted with this technology, even if you didn’t realize it.
From the facial recognition software that unlocks smartphones, to mobile check depositing systems on banking apps, image recognition is everywhere.
A function that increases or decreases rapidly at first, but then steadily slows as time moves, can be called a logarithmic function.
For example, we can say that the number of cases of the ongoing COVID-19 pandemic follows a logarithmic pattern, as the number of cases increased very fast in the beginning and are now slowing a bit.
Continue reading A Comprehensive Guide To Logarithmic Regression
