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
With recent advances in both Android device cameras and AI-accelerated chip technology, it’s no wonder that some of the most innovative, transformative use cases for machine learning on Android come in the form of computer vision.
From understanding scenes and creating artistic masterpieces to tracking human movement and even changing hair color, the promise of real-time computer vision on Android is real.
Continue reading Machine Learning on Android: Computer Vision
For this article, I want to discuss a couple topics in the iOS world I’ve always found interesting: memory management, heaps, and stacks. I’ve never really understood the difference between them or how heaps and stacks relate to storing memory in Swift.
So, I decided to do some of my own research and dig deep into memory management in Swift. I hope you’re looking forward to this topic as much as I am! Let’s dive right in.
So you’ve trained your Core ML model in Create ML or Turi Create. And now, you want to test it out on some real-world data. To make quick and dirty tests, you can leverage Swift Playgrounds and run Core ML models there. If you’re satisfied with the results, you can move over to Xcode project and run on-device or on a simulator.
Continue reading How to Run and Test Core ML Models in a Swift Playground
This post is a part of a series about feature engineering techniques for machine learning with python.
You can check out the rest of the articles:
Welcome back! In this post, we’re going to cover the different imputation techniques used when dealing with missing data. Additionally, we’ll also explore a few code snippets you can use directly in your machine learning and data science projects.
Continue reading Hands-on with Feature Engineering Techniques: Imputing Missing Values
