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This article is part 4 of a series centered on hands-on approaches to feature selection techniques. If you’ve missed any of the other posts, I’d recommend checking them out:

Welcome back! In part 4 of our series, we’ll provide an overview of embedded methods for feature selection.

We learned from the previous article a method that integrates a machine learning algorithm into the feature selection process. Those wrapper methods provide a good way to ensure that the selected features are the best for a specific machine learning model.

Continue reading Hands-on with Feature Selection Techniques: Embedded Methods

Over the years, Apple has released some breakthrough features at its annual WWDC conference. In addition to the iOS community, developers all over the world keenly look forward to Apple’s annual conferences. It’s no wonder that figuring out which WWDC conference stood out from the rest is always a dilemma.

Some say WWDC 2019 was the best developer conference in years, due to the slew of new features and significant tools introduced. SwiftUI, a powerful new framework for building user interfaces, and major upgrades in the Core ML and Vision framework make it tricky to downplay Apple’s achievements in 2019 — and I won’t do that either.

Continue reading Exploring Core Image: Apple’s First Computer Vision Framework

Table of contents:

A. Overview of sets
B. How sets work under the hood
C. Operations for sets
D. Collection type implementation in Swift (copy-on-write)

Continue reading Diving Into Data Structures In Swift: Sets

Today, we’re excited to launch Fritz Hair Segmentation, giving developers and users the ability to alter their hair with different colors, designs, or images.

Try it out for yourself on Android. You can download our demo app on the Google Play Store to play around with hair coloring.

Continue reading Embrace your new look with Fritz Hair Segmentation—Now available for Android developers

In the last piece in this series on developing with Flutter, we looked at how we can implement [image labeling using ML Kit, which belongs to the Firebase family.

In this 7th installment of the series, we’ll keep working with ML Kit, this time focusing on implementing face detection. The application we build will be able to detect human faces in an image, like so:

Continue reading Face Detection in Flutter Using Firebase’s ML Kit

Nowadays, lots of mobile applications like Office Lens and Genius Scan can turn a physical document into a digital one using only a phone—you actually no longer need to have a bulky, costly scanner.

Continue reading Comparing Apple’s and Google’s on-device OCR technologies

As most ML practitioners realize, developing a predictive model in Jupyter Notebook and making the predictions with excel data may not help you build the predictive models required at enterprise scale. To build the model at such a scale, you will need to consider several requirements and use various tools/frameworks that are especially designed to meet the purpose of this expansion.

Continue reading Enterprise Scale ML Jumpstart Kit — FastAI + RabbitMQ + Docker

The objective of data science projects is to make sense of data to people who are only interested in the insights of that data. There are multiple steps a Data Scientist/Machine Learning Engineer follows to provide these desired results. Data pre-processing (Cleaning, Formatting, Scaling, and Normalization) and data visualization through different plots are two very important steps that help in building machine learning models more accurately.

Continue reading Data Pre-processing and Visualization for Machine Learning Models

Basically, segmentation is a process that partitions an image into regions. It is an image processing approach that allows us to separate objects and textures in images. Segmentation is especially preferred in applications such as remote sensing or tumor detection in biomedicine.

There are many traditional ways of doing this. For example; point, line, and edge detection methods, thresholding, region-based, pixel-based clustering, morphological approaches, etc. Various methods have been developed for segmentation with convolutional neural networks (a common deep learning architecture), which have become indispensable in tackling more advanced challenges with image segmentation. In this post, we’ll take a closer look at one such architecture: u-net.

Continue reading Deep Learning for Image Segmentation: U-Net Architecture