Powering applications with the ability to understand the natural language of the text always amazes me. Apple made some significant strides with its Natural Language framework last year (2019). Specifically, the introduction of a built-in sentiment analysis feature can only help build smarter NLP-based iOS Applications.
Besides the improvements to the Natural Language framework, SwiftUI and Combine were the two biggies that were introduced during WWDC 2019.
In this article, we’ll be learning the following:
Object detection can be defined as a branch of computer vision which deals with the localization and the identification of an object. Object localization and identification are two different tasks that are put together to achieve this singular goal of object detection.
Object localization deals with specifying the location of an object in an image or a video stream, while object identification deals with assigning the object to a specific label, class, or description. With computer vision, developers can flexibly do things like embed surveillance tracking systems for security enhancement, real-time crop prediction, real-time disease identification/ tracking in the human cells, etc.
Continue reading Real-time Object Detection using SSD MobileNet V2 on Video Streams
Managing user authentication flows in your mobile apps is often a fundamental requirement to allow only authorized users to access data. The react-navigation library in its latest version (version 5) allows you to implement a custom authentication flow in React Native apps.
In this tutorial, we’ll discuss one of the strategies to implement an authentication flow using react-navigation library, and react-native-firebase.
A random forest is an ensemble model that consists of many decision trees. Predictions are made by averaging the predictions of each decision tree. Or, to extend the analogy—much like a forest is a collection of trees, the random forest model is also a collection of decision tree models. This makes random forests a strong modeling technique that’s much more powerful than a single decision tree.
Continue reading Random Forest Regression in Python Using Scikit-Learn
Mobile developers have a lot to gain from revolutionary changes that on-device machine learning can offer. This is because of the technology’s ability to bolster mobile applications—namely, allowing for smoother user experiences capable of leveraging powerful features, such as providing accurate location-based recommendations or instantaneously detecting plant diseases.
Continue reading Machine Learning and the Future of Mobile App Development
As a university student, I (and many of my peers) think a lot about exams. Sometimes I catch myself thinking: The human brain works 24/7 for 365 days, right from our birth, until…we step into the examination hall 🙂
How many times in your life you have wondered whether you will pass or fail an exam of some kind? Maybe you know more about yourself, your habits and tendencies, etc., so you can more accurately predict your result. But how many times you have wondered whether a friend of yours will pass the same exam or not?
Continue reading Logistic Regression in Python Using Scikit-learn
TensorFlow is a well-established player in the deep learning framework field. First, it works pretty damn well. Second, it’s a Google product that’s easily found throughout their own ecosystem (including Android, Chrome, and a slew of apps). Lastly, the TensorFlow team been very respectful of and responsive to both the researcher community as well as the commercial/production community.
YOLO stands for You Only Look Once. It’s an object detection model used in deep learning use cases, of which there are mainly 2 main families:
The idea of one-stage detection (also referred to as one-shot detection) is that you only look at the image once.
Stating that it was simply a bit better than YOLOv2, but not much changed.
YOLOv4 was then recently introduced as the “Optimal Speed and Accuracy of Object Detection”.
Naive Bayes algorithms are a set of supervised machine learning algorithms based on the Bayes probability theorem, which we’ll discuss in this article. Naive Bayes algorithms assume that there’s no correlation between features in a dataset used to train the model.
In spite of this oversimplified assumption, naive Bayes classifiers work very well in many complex real-world problems. A big advantage of naive Bayes classifiers is that they only require a relatively small number of training data samples to perform classification efficiently, compared to other algorithms like logistic regression, decision trees, and support vector machines.
Continue reading Naive Bayes Classifier in Python Using Scikit-learn
At WWDC 2018, Apple introduced a brand new framework for iOS developers called Natural Language. Simply put, this framework gives apps the ability to analyze natural language text and understand parts of it. Natural Language can perform a variety of tasks on a block of text by assigning tag schemes to the text. What does this mean?
Continue reading Natural Language in iOS 12: Customizing tag schemes and named entity recognition
