iOS’s Natural Language framework allows us to analyze language and to perform language-specific tasks like script identification, tokenization, lemmatization, part-of-speech tagging, and named entity recognition.
In this introduction tutorial, we will discover this framework’s capabilities by looking at 4 common and essential techniques:
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The education industry has embraced recent technological advancements with open arms with the inclusion of online modules, topic-based discussion forums, and the option of communicating with lecturers after hours.
While these developments have made the learning process more comprehensive and simplified for students, there’s still a lot of untapped potential in the industry.
Machine learning is blazing paths for new, more personalized learning experiences that have the potential to improve student engagement, create clearer communication channels between lecturers and students, and to develop less biased grading systems.
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At the recent TensorFlow Dev Summit, Google announced upcoming support on the TensorFlow platform for Swift. Their goal is to make it easier to use machine learning libraries and help catch more mistakes before running ML code.
Swift for TensorFlow — and some new Swift extensions planned for the upcoming Swift 4.2 release — will let you execute arbitrary Python code including scientific packages like NumPy, making it simple to port existing TensorFlow Python code to Swift.
The Levenshtein distance is a text similarity metric that measures the distance between 2 words. It has a number of applications, including text autocompletion and autocorrection.
For either of these use cases, the word entered by a user is compared to words in a dictionary to find the closest match, at which point a suggestion(s) is made. The dictionary may contain thousands of words, and thus the response of the application for comparing 2 words will likely take a few milliseconds.
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The least-squares method for classification is based on linearly separating 2 or more classes. In this article, I’m going to show you how to create a Python program to classify images with digits from 0–9 using only NumPy and PIL.
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Sometimes you might want to change part of your lenses and replace them with an image or object or even an effect. Segmentation allows you to do that while using segmentation textures.
For instance, you can change the background by adding the texture you want or changing the user’s hair by adding color or a texture to it.
Types of segmentation textures include portrait background, portrait hair, portrait shoulder, portrait face, portrait head, sky, and body. We will look at what they do later on in this article.
So you came here—let me guess—it’s either you’re in a data science competition and you read somewhere about how winners of most competitions win with ensembles, or you’re just a curious data scientist who wants to learn about ensembles.
Either way, understanding how ensembles work is a very important knowledge and as data scientists and machine learning engineers, you should be able to employ the skills behind them.
Research has shown that a majority of the time, ensembles will outperform a single model, and it’s the recommended technique for maximizing accuracy or reducing errors in a machine learning model.
In this tutorial, we’ll cover how to implement the neural-style algorithm that’s based on this paper.
What is neural style transfer?Neural style transfer is a technique used to generate images in the style of another image. The neural-style algorithm takes a content-image as input, a style image, and returns the content image as if it were painted using the artistic style of the style image.
LUT or, Look Up Table, is a photo or image filter that enhances and changes the color tone and grading of your image. It essentially can convert colors and details in a source file to a new destination state.
Using a custom lookup table or “LUT” allows us to have free range on creating our own custom color corrections through external third party programs and bring them directly into Lens Studio.
Machine learning (ML) began its ascent into the medical industry when it acquired the ability to detect visual patterns between images—a skill doctors and technicians take years to master.
Specifically, ML models for computer vision tasks in the medical field train on datasets of separated images to learn to recognize their similarities and differences.
Continue reading “Detecting Skin Cancer on iOS with Xcode and Create ML”
