Here’s a list of my mobile ML in iOS resources posted on Better Programming:

Machine Learning — Core ML, Create ML, and Natural Language

• Core ML 3 On Device Training — Part 1: Build Updatable Model, Part 2: Re-train On Device — What if a cat vs. dog image classification model gave incorrect results? This tutorial shows how to re-train and update the model on your device with the correct data. Was published in 2019, so might require an updated MLModel 

• PencilKit Meets Core ML — Recognizing digits from drawing using MNIST

• Build a Core ML Movie Recommender SwiftUI App Using Create ML — Leverage CreateML’s recommender template to predict the top K movie recommendations based on user ratings in a SwiftUI application

• Sound Classifier Using Core ML and Create ML — Recognize by voice in a live audio stream using the SoundAnalysis framework with your own Core ML model built using Create ML

• Classify Movie Reviews Using Natural Language Framework — By using built-in sentiment analysis and NLP’s Text classification on the Rotten Tomatoes dataset.

• Sentiment Analysis of Hacker News feed — Using Apple’s built-in sentiment score for NLP, we see that most top stories attract polarizing opinions on Hacker News.

• Build a SwiftUI + Core ML Emoji Hunt Game for iOS — Let’s create a fun machine-learning iOS camera app that lets you search for things in your house that are similar to emojis

• Core ML Background Removal — Use the DeepLabV3 image segmentation model to add, remove, and modify image backgrounds in your iOS app

• Train and Run a Create ML Style Transfer Model in an iOS Camera Application

Computer Vision — Core Image and Vision Framework

• iOS 13 Vision Text Document Scanner

• Image Similarity Using Feature Prints — Determine the Euclidean distance between images using their feature prints

• Scanning Credit Card Using Rectangle Detection and Text Recognition

• Cropping Areas Of Interest using Saliency — Extract salient features using the upgraded Vision framework

• Determine the Best Facial Expression in Live Photos — Using the VNDetectFaceCaptureQualityRequest

• Coin Detection —Simplify the contours by leveraging Core Image filters for pre-processing the images before passing them on to the Vision request. We’ll look to mask the images in order to reduce texture noise

• Swipeless Tinder Using Hand Pose Estimation — Detect hand gestures to swipe 👍 👎

• Person Segmentation — Separate people from backgrounds in images and videos

• Anomaly Detection and Watermark Extraction Using Core Image — Use a Core Image filter to find differences and anomalies between images

RealityKit

• Entities, Gestures, and Ray Casting — Leveraging Vision and PencilKit frameworks.

• Detect and Handle Collision Events in a RealityKit Scene Across Different Entities