Hi there, this is the 3rd part of a 3 part series, for better understanding kindly read my first and second articles here:
In part 3, we’ll be taking the model we built in parts 1 and 2 and exploring how to implement it on a Raspberry Pi 3.
Raspberry Pi is a low-cost micro computer that can be plugged into a computer monitor or TV alongside a keyboard and mouse to simulate a full PC experience.
Continue reading End-to-End Object Detection Using EfficientDet on Raspberry Pi 3 (Part 3)
In certain cases, there are requirements where a certain page in your application can only be viewed in a particular orientation, i.e. portrait/landscape. Dealing with this issue in native Android/iOS is like a walk in the park, but doing this in a cross-platform framework can be confusing!
Want to find the easiest way out of it? This tutorial is intended to show you how to force an orientation on a single page while developing with Xamarin. Let’s get to it!
Continue reading Force an orientation on a single page in Xamarin.Forms
Machine learning as a field is full of technical terms, making it difficult for beginners to get started. One might see things like “deep learning,” “the kernel trick,” “regularization,” “overfitting,” “semi-supervised learning,” “cross-validation,” etc. But what in the world do they mean?
One of the core tasks in building any machine learning model is to evaluate its performance. It’s fundamental, and it’s also really hard.
Continue reading Evaluation Metrics for Machine Learning Models
In today’s world, mobile devices are an essential part of our daily lives—we use them as we walk, run, drive, and when doing many other activities.
Understanding what users are doing when holding their phones can allow your apps to adapt intuitively based on those user actions. And for some apps, it might be a requirement to identify when a user starts or stops a particular activity. For example, an app can start music in the background if a user starts running, or it can calculate the number of calories burned.
Continue reading Detect Users’ Activity in Android Using the Transition API
This post is a beginner’s guide for understanding the inner workings of doc2vec for NLP tasks.
There are two primary architectures for implementing doc2vec: namely Distributed Memory Model of Paragraph Vectors (PV-DM) and Distributed Bag-of-Words version of Paragraph Vector (PV-DBOW).
In this article, we’ll dive into how doc2vec is related to word2vec and explore both of their architectures while also getting some hands-on experience with them.
We’ve seen this feature in countless smartphone ads. The quick scene has someone traveling to a foreign country, having a local say something into their smartphone, and then smile as it provides the translation for them. Usually it takes up just seconds of the ad before they move onto a new camera feature or something.
What they don’t show is the aftermath as that smile turns into panic. For soon after, the user realizes they’re nowhere near their hotel’s free WiFi. They begin to calculate how much their international data charge will be by the time they finally translate the local’s instructions to the best food in town. Who knows if they’ll be able to afford it by then?
Continue reading Data-Free Speech Translator using SFSpeechRecognizer and ML Kit on iOS
In the last article, we discussed 3 general and common situations faced when handling data: optimizing how to read CSV files with a lot of unnecessary columns, using the map method to create new category columns, and finding empty strings in your DataFrame that aren’t labeled as null.
Continue reading Data Handling Scenarios Part 2: Working with Missing Values in a Dataset
Until recently, there were very few libraries available for performing machine learning on mobile platforms. But these days we’re seeing loads of libraries being released. Last year, Apple released Core ML at WWDC, and it was considered a big step for the field of mobile ML.
Continue reading Core ML vs ML Kit: Which Mobile Machine Learning Framework Is Right for You?
Nowadays, people check their watches for important notifications as much as they do the time. Wearable devices, like Android-based watches, are gaining more momentum as Google continues to improve its Android Wear platform for developers.
A couple of months ago, I was working on a simple application called Location-based tasks using Geofence. If you don’t know what Geofencing is, check out the link below:
Continue reading Connect Android Devices with a Wear OS Emulator
Welcome back to the second part of this series. In this section, we’ll dive into the YOLO object localization model.
Although you’ve probably heard the acronym YOLO before, this one’s different. For the purposes of this post, YOLO stands for “You Only Look Once”.
Continue reading Gentle guide on how YOLO Object Localization works with Keras
