This post is a part of a series about feature engineering techniques for machine learning with Python.
Hey again! In this post, we’re going to explore feature scaling transformations for feature engineering.
Let’s start with feature magnitude. Frequently, our dataset contains features that highly vary in scales, and that’s a problem for some algorithms that rely on distance.
Continue reading “Hands-on with Feature Engineering Techniques: Feature Scaling”
In the previous article of this series on developing Flutter applications with TensorFlow Lite, we looked at how we can develop digit recognizer with Flutter and TensorFlow Lite and image classification with Flutter and TensorFlow Lite.
In the third article of this series, we’ll keep working with TensorFlow Lite, this time focusing on implementing object detection. The application we are going to build will be able to recognize objects presented in an image.
Continue reading “Object Detection with Flutter and TensorFlow Lite”
Pam Taylor is just 16, but already making waves in the AR world. A natural creative, she’s made lenses based on breakfast cereal, makeup trends, and dream vacations.
I was able to catch up with the well-spoken, talented creator who’s already an official Lens Studio Creator and a soon-to-be AR megastar.
Continue reading “Snapchat Lens Creator Spotlight: Pam Taylor”
During WWDC 2019, Create ML got its own independent identity. It’s like an origin story of some sort, giving way to a whole new range of capabilities. Create ML is now a separate application that’s shipped with Xcode 11.
Apple once again, through Create ML, showcases how serious they are about machine learning on mobile. Their recent advancements with the Create ML framework are a big boon for mobile machine learning developers, given that these improvements allow them to easily create their own models without much machine learning expertise.
Continue reading “Sound Classification on iOS Using Core ML 3 and Create ML”
OpenCV is an open source computer vision and machine learning library. It has 2500+ optimized algorithms—a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has many interfaces, including Python, Java, C++, and Matlab.
Here, we’re gonna tackle the Python interface.
Continue reading “OpenCV-Python Cheat Sheet: From Importing Images to Face Detection”
Mobile machine learning is still, relatively speaking, in its infancy. As such, new developer tools, frameworks, and environments are regularly in flux.
It can be hard to keep up with the rapid advancements happening at this emerging intersection.
Continue reading “Mobile Machine Learning: Development Tools and Frameworks”
Pneumonia is an inflammatory condition of the lung affecting the alveoli. Typically, symptoms include some combination of productive or dry cough, chest pain, fever, and trouble breathing. According to UNICEF pneumonia remains the leading infectious cause of death among children under five, killing approximately 2,400 children a day.
Early diagnosis of this treatable condition is very important, as it could facilitate earlier treatment and hopefully result in improved clinical outcomes. There are several tests used to confirm diagnoses, including analysis of chest X-ray images.
Continue reading “Detecting Pneumonia in an iOS App with Create ML”
While working with a huge list of data, it’s often convenient to quickly go through the list by means of a scrollbar. As a matter of fact, while reading this article itself, you can see a scrollbar on the right side of the page, which you can use to quickly navigate to the end of the page.
This design pattern is quite common, and Android is no exception, as we can easily find numerous examples of scrollbars, especially when the content is being displayed in a list. Y
ou can see some examples of this below:
Continue reading “Adding fast scroll to RecyclerView in Android”
Imagine we have a set of labeled and unlabeled data, and we want to build a classifier which takes the unlabeled data as input and labels that data as output.
With this kind of situation, we’ll need to build a classification model that will learn from already-labeled data (training data). Later we’ll use that model to predict our unlabeled data (test data).
This type of machine learning is called supervised learning, which we can define as feeding data into a machine learning algorithm.
In doing so, we’re actually showing that groups exist, and which data belong to which groups.
Continue reading “Implementing K-Nearest Neighbors in Your Machine Learning Model”
Have you ever played mobile games like Subway Surfers or Temple Run and wondered how the position of the runner changes?
Have you ever counted the number of steps/distance you’ve walked in a day? Have you ever used a compass in your phone and wondered how it works?
Or ever thought about while talking to a person through your phone, how the phone’s screen goes black when you bring it close to your ear?
Continue reading “Sensors in Android – Everything You Need to Know”
