[모바일 머신러닝 라이브러리 및 자료] 

Awesome-Mobile-Machine-Learning

A list of awesome mobile machine learning resources curated by Fritz AI.

About Fritz AI

Fritz AI helps you teach your applications how to see, hear, sense, and think. Create ML-powered features in your mobile apps for iOS, Android, and SnapML. Start with our ready-to-use feature APIs or use our Studio to build your own custom models—without code.

Sign up for a Fritz AI account to start building today.

Table of Contents

• Getting Started
  • (Mostly) Non-Code Primers on Mobile Machine Learning
  • Getting Started with Data Science and Machine Learning
• Mobile Machine Learning Frameworks
  • Mobile-Ready
  • Mobile-Compatible
• Code, Libraries, and Resources
  • SnapML
  • iOS
  • Android
  • Browser
  • Server Side
  • Fritz Community Resources
• Tutorials & Learning
  • SnapML
  • iOS
  • Android
  • Cross/Multi-Platform and Edge
  • Online Courses, Videos, and E-Books
  • Publications to Follow

Getting Started

(Mostly) Non-Code Primers on Mobile Machine Learning

• Machine learning on mobile: What can you actually do with it?
• Machine Learning and the Future of Mobile App Development
• Machine Learning on Mobile Devices: 3 Steps for Deploying ML in Your Apps
• Embracing Machine Learning as a Mobile Developer
• End-to-End Mobile Machine Learning with Fritz AI: A Non-Developer’s Journey
• Machine Learning on iOS and Android
• Deep Learning on the Edge
• Why Machine Learning on the Edge
• 5 App Ideas to Unleash the Power of Mobile Machine Learning
• How TensorFlow Lite Optimizes Neural Networks for Mobile Machine Learning
• Machine Learning for Mobile - EBook
• Why the Future of Machine Learning is Tiny
• Machine Learning on mobile: on the device or in the cloud?
• Cameras that understand: Portrait Mode and Google Lens
• Machine Learning App Development—Disrupting the Mobile App Industry
• How smartphones handle huge neural networks
• How to Fit Large Neural Networks on the Edge
• Advances in Machine Learning Are Revolutionizing the Mobile App Development Realm
• No cloud required: Why AI’s future is at the edge
• Comparing Mobile Machine Learning Frameworks
• How AI Accelerators Are Changing The Face Of Edge Computing
• Embedded and mobile deep learning research resources
• On-Device AI: MIT Technology Review Hub
• The 5 Algorithms for Efficient Deep Learning Inference on Small Devices
• PyTorch Mobile: Exploring Facebook’s new mobile machine learning solution
• AI in the Browser
• Federated Learning: An Introduction
• Popular Mobile Machine Learning Projects to Help You Start Building
• Federated Learning: Motivation and Challenges
• Will NVIDIA GPUs push AI on mobile devices to the next level?
• Mobile Machine Learning: 2020 Year in Review
• Alibaba’s Mobile Neural Network: A deep learning framework for mobile and embedded devices
• Monetizing Mobile Machine Learning
• The State of Mobile Machine Learning in 2020 (Industry Report)
• Lens Studio 3.0 introduces SnapML for adding custom neural networks directly to Snapchat
• SnapML in Lens Studio: Using custom machine learning models to power AR experiences

Getting Started with Data Science and Machine Learning

• New to Data Science? Here are a few places to start
• Machine Learning Crash Course—Google
• Siraj Raval’s YouTube Channel
• Fast.ai
• Kaggle: The place to do data science projects
• Awesome Data Science with Python: A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks.

Mobile Machine Learning Frameworks

Mobile-Ready

• Fritz AI: Fritz AI is the machine learning platform for iOS, Android, and SnapML developers/creators. Teach your mobile devices to see, hear, sense, and think.
• Core ML: With Core ML, you can integrate trained machine learning models into your iOS apps.
• TensorFlow Lite: TensorFlow Lite is an open source deep learning framework for on-device inference.
• Create ML: Use Create ML with familiar tools like Swift and macOS playgrounds to create and train custom machine learning models on your Mac.
• Turi Create API: Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your iOS app.
• ML Kit: ML Kit beta brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
• PyTorch Mobile: PyTorch Mobile is a new framework for helping mobile developers and machine learning engineers embed PyTorch ML models on-device.
• QNNPACK: QNNPACK (Quantized Neural Networks PACKage) is a mobile-optimized library for low-precision high-performance neural network inference. QNNPACK provides implementation of common neural network operators on quantized 8-bit tensors.

Mobile-Compatible

• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. EASY
• ONNX: ONNX is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. EASY
• Microsoft Cognitive Toolkit: The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. HARD
• IBM Watson: Watson is IBM’s suite of enterprise-ready AI services, applications, and tooling. EASY
• Caffe2: A lightweight, modular, and scalable deep learning framework. HARD
• Apache MXNet: A fast and scalable training and inference framework with an easy-to-use, concise API for machine learning. HARD
• PyTorch: An open source deep learning platform that provides a seamless path from research prototyping to production deployment..HARD

Code, Libraries, and Resources

SnapML

• Lens Studio SnapML documentation
• Fritz AI for SnapML documentation
• Lens Studio support forum, tutorials, discussion, etc

iOS

• fritz-examples: A collection of experiences utilizing machine learning models from Fritz AI
• swift: Swift for TensorFlow Project Home Page.
• swift-models: Models and examples built with Swift for TensorFlow.
• swift-apis: Swift for TensorFlow Deep Learning Library.
• Swift-AI: Swift AI includes a collection of common tools used for artificial intelligence and scientific applications on iOS and macOS.
• Serrano: A Swift deep learning library with Accelerate and Metal support.
• Revolver: A framework for building fast genetic algorithms in Swift.
• fantastic-machine-learning: A curated list of machine learning resources, preferably, mostly focused on Swift/Core ML.
• awesome-ml-demos-with-ios: We tackle the challenge of using machine learning models on iOS via Core ML and ML Kit (TensorFlow Lite).
• Awesome-CoreML-Models: the largest collection of machine learning models in Core ML format. Also includes model conversion formats, external collections of ML models, and individual ML models—all of which can be converted to Core ML.
• iOS_ML: List of Machine Learning, AI, NLP solutions for iOS.
• Awesome-Design-Tools: A curated list of the best design tools and frameworks for iOS and macOS.
• awesome-ios: A curated list of awesome iOS ecosystem, including Objective-C and Swift Projects.
• List-CoreML-Models: A list of Core ML models, projects, and resources.
• coremltools: Core ML community tools contains all supporting tools for CoreML model conversion and validation. This includes Scikit Learn, LIBSVM, Caffe, Keras and XGBoost.
• Bender: Bender is an abstraction layer over MetalPerformanceShaders useful for working with neural networks.
• StyleArt: The Style Art library processes images using Core ML with a set of pre trained machine learning models and converts them to different art styles.
• LocoKit: Location, motion, and activity recording framework for iOS; includes the ability to classify device activity by mode of transport.
• awesome-tflite: A curated list of awesome TensorFlow Lite models, samples, tutorials, tools and learning resources.
• googlesamples / mlkit: A collection of quickstart samples demonstrating the ML Kit APIs on Android and iOS.

Android

• fritz-examples: A collection of experiences utilizing machine learning models from Fritz AI
• awesome-android: A curated list of awesome Android packages and resources.
• awesome-java: A curated list of awesome frameworks, libraries and software for the Java programming language.
• AndroidTensorFlowMachineLearningExample: Android TensorFlow MachineLearning Example (Building TensorFlow for Android).
• onyx: An android library that uses technologies like artificial Intelligence, machine learning, and deep learning to make developers understand the content that they are displaying in their app.
• android-malware-analysis: This project seeks to apply machine learning algorithms to Android malware classification.
• awesome-tflite: A curated list of awesome TensorFlow Lite models, samples, tutorials, tools and learning resources.
• googlesamples / mlkit: A collection of quickstart samples demonstrating the ML Kit APIs on Android and iOS.
• TengineKit: Free Real-Time Face Landmarks - 212 Points For Mobile

Browser

• tfjs-models: Pretrained models for TensorFlow.js
• magenta-js: Music and Art Generation with Machine Intelligence in the Browser
• tfjs-node: TensorFlow powered JavaScript library for training and deploying ML models on Node.js
• tfjs-examples: Examples built with TensorFlow.js

Server Side

• awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software.
• awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.
• my-awesome-ai-bookmarks: Curated list of reads, implementations, and core concepts of Artificial Intelligence, Deep Learning, and Machine Learning.
• datasets: A collection of datasets ready to use with TensorFlow

Fritz AI Community Resources

• Mobile ML GitHub Repositories: List of repos with machine learning models ready for mobile, organized by feature type.
• Image Recognition Guide: Almost everything you need to know about how image recognition works.
• Object Detection Guide: Almost everything you need to know about how object detection works.
• Image Segmentation Guide: Almost everything you need to know about how image segmentation works.
• Pose Estimation Guide: Almost everything you need to know about how pose estimation works.
• Style Transfer Guide: Almost everything you need to know about how style transfer works.
• AI Startup Landscape: The AI and Machine Learning landscape is rapidly changing. Here’s a list of current organizations and tools, organized by ML lifecycle stage.
• AI and Machine Learning Newsletters: Explore a collection of helpful AI and ML newsletters.
• Mobile Development Newsletters: Explore a collection of helpful mobile development newsletters.
• Data Science Newsletters: Explore a collection of helpful data science newsletters.
• Facebook Groups: See our list of Facebook groups for AI and ML, mobile dev, data science, and programming.

Tutorials & Learning

SnapML

Image Recognition/Classification

• Create a Custom AI-Powered Snapchat Lens with Fritz AI Studio
• Xbox or PS5 Enthusiast? — Create an AI-Powered Snapchat Lens with Fritz AI Studio

Object Detection

• Working with SnapML Templates: Object Detection
• (Guide) Object Detection ML

Image Segmentation

• Building a Custom Glasses Snapchat Lens with Fritz AI and Lens Studio — Zero Code Involved
• Building a Custom Face Mask Snapchat Lens with Fritz AI and Lens Studio
• SnapML in Lens Studio—Apply AR Effects to Segmented Objects
• Create Lenses with SnapML & Fritz.ai (YouTube)

Style Transfer

• Working with SnapML Templates in Lens Studio: Style Transfer
• Creating a Style Transfer Snapchat Lens with Fritz AI and SnapML in Lens Studio
• Combining Style Transfer with Background Replacement in Lens Studio with Fritz AI and SnapML
• Create a Style Transfer Snapchat Lens with SnapML’s ML Component
• (Guide) Style Transfer ML
• Adjusting Style Transfer Lenses for SnapCamera
• Quickly Build a Snapchat Lens By Leveraging Fritz AI Studio’s Style Transfer Model

Other

• (Ebook) Working with Machine Learning in Snap Lens Studio
• Demystifying common use cases for SnapML in Lens Studio
• Working with SnapML Templates in Lens Studio: An Overview
• Exploring SnapML: A Technical Overview
• Machine Learning Primer for Snapchat Lens Creators
• SnapML in Lens Studio: ML Component Breakdown

iOS

Computer Vision

Image Recognition/Classification

• Intro to machine learning on iOS: Using Core ML to recognize handwritten digits
• Building Not Hotdog with Turi Create and Core ML—in an afternoon
• Core ML SImplified with Lumina
• CoffeeBot—Using Scikit-learn, Core ML for iOS, and Alexa to predict the right coffee to drink
• Emotion detection for cats—Custom Vision & Core ML on a Swift Playground
• Building an iOS camera calculator with Core ML’s Vision and Tesseract OCR
• Using Core ML and Vision in iOS for Age Detection
• Using Core ML and Custom Vision to Build a Real-Time Hand Sign Detector in iOS
• Using Core ML and ARKit to Build a Gesture-Based Interface iOS App
• Making a “Pokedex” for iOS Using Create ML and Core ML with Vision
• Moving AI from the Cloud to the Edge with Crowd Count and Apple’s Core ML
• Introduction to Core ML: Building a Simple Image Recognition App
• Building a real-time object recognition iOS app that detects sushi
• Detecting Pneumonia in an iOS App with Create ML
• Training a Core ML Model with Turi Create to Classify Dog Breeds
• Recreate Dominos Points for Pies app on iOS with Fritz Image Labeling
• Detecting Whisky brands with Core ML and IBM Watson services
• Powering an iOS app with ML: How to get started using Create ML and Core ML
• Machine Learning in iOS: Azure Custom Vision and Core ML
• Machine Learning in iOS: Turi Create and Core ML
• Creating a Custom Core ML Model Using Python and Turi Create
• Evaluate Construction Site Safety on iOS using Machine Learning
• Logo Recognition iOS Application Using Machine Learning and Flask API
• PyTorch Mobile: Image Classification on iOS
• Building a Barcode Scanner in Swift on iOS
• Incorporating machine learning into iOS apps
• Building a multi-class image classifier on iOS
• On-Device Machine Learning with SwiftUI and PyTorch Mobile
• Building an on-device face mask detector with Fritz AI Studio
• Build a SwiftUI + Core ML Emoji Hunt Game for iOS
• Implementing a Fritz AI Machine Learning Model in an iOS app
• Train a Face-Mask Detection Model in Under 5 Minutes using Lobe.ai
• Detecting Skin Cancer on iOS with Xcode and Create ML
• Image Labeling in Android with Fritz AI

Object/Face Detection

• Hand Detection with Core ML and ARKit
• MobileNetV2 + SSDLite with Core ML (Object Detection)
• Building a simple lane detection iOS app using OpenCV
• Live Face Tracking on iOS using Vision Framework
• Unity AR Foundation and CoreML: Hand detection and tracking
• MakeML’s Automated Video Annotation Tool for Object Detection on iOS
• Adding custom object detection to an iOS app with Turi Create and Fritz AI
• License Plate Recognition, Detection, and Plate Number Extraction on iOS
• Build a Touchless Swipe iOS App Using ML Kit’s Face Detection API
• Scanning Credit Cards with Computer Vision on iOS
• Face Recognition and Detection on iOS Using Native Swift Code, Core ML, and ARKit
• Using TensorFlow.js in a Native iOS App to Perform Object Detection
• Create Homemade Recipes of Your Favorite Products on iOS Using Fritz AI Studio
• Automatically Pixelate Faces on iOS using Face Detection with Native Swift Code

Style Transfer

• Real-Time Style Transfer for iOS
• Creating a Prisma-like App with Core ML, Style Transfer, and Turi Create
• Style Transfer iOS Application Using Convolutional Neural Networks
• Train and Run a Create ML Style Transfer Model in an iOS Camera Application

Image Segmentation

• Build your own Portrait Mode on iOS using machine learning in < 30 minutes
• Try on a new style—Build an iOS app to change your hair color with Fritz Hair Segmentation
• MakeML: Nail Segmentation on iOS
• Simple Semantic Image Segmentation in an iOS Application — DeepLabV3 Implementation
• Semantic and Instance Segmentation on iOS Using a Flask API — DeepLabV3+ and Mask R-CNN
• Using Fritz AI in SwiftUI

Pose Estimation

• Pose Estimation on iOS
• Human pose estimation on images for iOS
• “Just Point It”: Machine Learning on iOS with Pose Estimation + OCR Using Core ML and ML Kit
• Swipeless Tinder Using iOS 14 Vision Hand Pose Estimation

Text Recognition

• Integrating Google ML Kit in iOS for Face Detection, Text Recognition, and Many More
• Using Vision Framework for Text Detection in iOS 11
• Vision in iOS: Text detection and Tesseract recognition
• Comparing iOS Text Recognition SDKS Using Delta
• Text recognition on iOS 13 with Vision, SwiftUI and Combine
• License Plate Recognition, Detection, and Plate Number Extraction on iOS
• Scanning Credit Cards with Computer Vision on iOS

Other

• Photo Stacking in iOS with Vision and Metal
• Computer Vision in iOS: Determine the Best Facial Expression in Live Photos
• Compute Image Similarity Using Computer Vision in iOS
• Introduction to XGBoost with an Implementation in an iOS Application
• Implement Depth Estimation on iOS Using a FCRN Model

Natural Language Processing

Text Classification

• Implementing a Natural Language Classifier in iOS with Keras + Core ML
• Train a Text Classification Model with Create ML
• Text Classification on iOS Using Create ML
• Classifying Movie Reviews With Natural Language Framework
• Easy Topic Classifier on iOS with Apple’s Natural Language Framework
• Using Create ML on iOS to auto-complete forms

Sentiment Analysis

• Training a Core ML Model for Sentiment Analysis
• Sentiment analysis with Natural Language and SwiftUI
• Using Core ML and Natural Language for Sentiment Analysis on iOS
• Processing Tweets Using Natural Language and Create ML on iOS
• Sentiment analysis with Natural Language and SwiftUI
• Introduction to natural language processing in Swift
• Sentiment Analysis on iOS Using Swift, Natural Language, and Combine: Hacker News Top Stories
• Sentiment Analysis iOS Application Using Hugging Face’s Transformers Library
• Build a Positive News iOS Application Using the Power of Machine Learning

Translation

• Text Recognition and Translation on iOS Using ML Kit and Google Translate
• Language Identification, Translation, and Smart Reply in iOS with Firebase ML Kit

NLP Tools and Techniques

• Natural Language on iOS 12: Customizing tag schemes and named entity recognition
• Introduction to Natural Language Processing in Swift
• Core ML with GloVe Word Embedding and a Recursive Neural Network
• 4 Techniques You Must Know for Natural Language Processing on iOS
• Exploring Word Embeddings and Text Catalogs with Apple’s Natural Language Framework in iOS
• Matching Natural Language Text for Predefined Data Patterns on Apple's Devices
• On-Device Video Subtitle Generation in SwiftUI

Speech / Audio

• Building a Sound Classification iOS Application using AI
• Data-Free Speech Translator using SFSpeechRecognizer and ML Kit on iOS
• iOS On-Device Speech Recognition
• Sound Classification on iOS Using Core ML 3 and Create ML
• Speech recognition and speech synthesis on iOS with Swift
• Recognizing Speech Locally on an iOS Device Using the Speech Framework
• Powering Accessibility on iOS with SwiftUI and Machine Learning

Model Conversion/Deployment/Management

• Reverse Engineering Core ML
• How to fine-tune ResNet in Keras and use it in an iOS app via Core ML
• iOS 12 Core ML Benchmarks
• Reducing Core ML 2 Model Size by 4X Using Quantization in iOS 12
• Using coremltools to Convert a Keras Model to Core ML for iOS
• Train and Ship a Core ML Object Detection Model for iOS in 4 Hours—Without a Line of Code
• Advanced Tips for Core ML
• Does my Core ML model run on Apple’s Neural Engine
• Instantly deploy your Core ML model on Maive without writing an iOS app
• Figuring out if Core ML models use the Apple Neural Engine
• Working with Create ML’s MLDataTable to Pre-Process Non-Image Data
• What’s New in Core ML 2
• Beginner’s Guide to Core ML Tools: Converting a Caffe Model to Core ML Format
• Custom Layers in Core ML
• Machine Learning in iOS: IBM Watson and Core ML
• Running Keras models on iOS with Core ML
• TensorFlow to Core ML conversion and model inspection
• An in-depth look at Core ML 3
• What's new in Core ML 3
• Hello, Core ML 3
• Designing Great Mobile ML Experiences
• On-device training with Core ML – part 1
• On-device training with Core ML - part 2
• Exploring the new ML Kit features on iOS using Swift
• Introduction to Machine Learning for iOS Developers
• How To Create Updatable Models Using Core ML 3
• Build iOS-ready machine learning models using Create ML
• Create ML for iOS— Increasing model accuracy
• Incorporating machine learning into iOS Apps
• Exploring Use Cases of Core ML Tools
• How to convert a NN model from TensorFlow Lite to CoreML
• MLDataTable: The Panda For iOS Developers
• How to convert images to MLMultiArray
• Swift loves TensorFlow and Core ML
• Upsampling in Core ML
• How to Get a Core ML Model to Produce Images as Output
• Core ML On-Device Training, with Transfer Learning from Swift for TensorFlow Models
• Protecting Core ML Models
• Core ML and Vision Tutorial: On-device training on iOS
• How to Run and Test Core ML Models in a Swift Playground
• Evolving Your iOS App’s Intelligence with Core ML Model Deployment

Other

• Ray Wenderlich iOS Machine Learning Tutorials
• Build a Core ML Recommender Engine for iOS Using Create ML
• Colorizing Images in an iOS App Using DeOldify and a Flask API
• Advancements in Artificial Intelligence in iOS 14
• Core ML + ARKit: Annotating objects in Augmented Reality

Android

Computer Vision

Image Recognition/Classification

• Introduction to Machine Learning on Android Part 2: Building an app to recognize handwritten digits
• Using TensorFlow Lite and ML Kit to Build a “Pokedex” in Android
• Visual Recognition in Android Using IBM Watson
• Building a Custom Machine Learning Model on Android with TensorFlow Lite
• Exploring Firebase ML Kit on Android: Barcode Scanning (Part 3)
• Using TensorFlow on Android—step by step code explanation
• Mobile intelligence—TensorFlow Lite Classification on Android
• How to apply Machine Learning to Android using Fritz
• Image Recognition with ML Kit
• Creating a Google Lens clone using Firebase ML Kit
• Applying TensorFlow in Android in 4 steps
• Using TensorFlow Lite and ML Kit to build custom machine learning models for Android
• Inspecting TensorFlow Lite image classification model
• Image Classification on Android with TensorFlow Lite and CameraX
• Image Labeling on Android in Kotlin using Fritz AI and CameraX
• Plant Disease Classification with TensorFlow Lite on Android - Part 1 and Part 2
• Testing TensorFlow Lite image classification model
• Image Classification on Android using OpenCV
• Image Classification on Android using a Keras Model Deployed in Flask
• PyTorch Mobile: Image classification on Android
• Build TensorFlow Lite model with Firebase AutoML Vision Edge
• Image Recognition for Android with a Custom TensorFlow Lite Model
• Working with TensorFlow Lite in Flutter
• TensorFlow Lite Model Maker: Build an Image Classifier for Android
• Digit Recognizer with Flutter and TensorFlow Lite
• Image Classification on Mobile with Flutter, TensorFlow Lite, and Teachable Machine

Object Detection

• Real-Time Face Detection on Android with ML Kit
• Creating an Android app with Snapchat-style filters in 7 steps using Firebase’s ML Kit
• Identifying and Counting Items in Real-Time with Fritz Object Detection for Android
• Exploring Firebase ML Kit on Android: Landmark Detection (Part 4)
• Detecting Pikachu on Android using TensorFlow Object Detection
• Building a pet monitoring app in Android with machine learning
• Building a real-time object detection app on Android using Firebase ML Kit
• Blink detection on Android using Firebase ML Kit’s Face Detection API
• Flutter Face Detection
• Solve WordSearch games with Android and ML Kit
• Object Detection in Android Using Firebase ML Kit
• Object Detection in Android with Fritz AI
• Turning the Mobile Camera into a Real-Time Object Detector with Flutter and TensorFlow Lite
• On-Device Face Detection on Android using Google’s ML Kit
• Object Detection in Android Using Firebase ML Kit
• Object Detection with Flutter and TensorFlow Lite

Style Transfer

• Real-Time Style Transfer for Android
• Style Transfer on Android in Kotlin using Fritz AI and CameraX
• Style Transfer in Android Applications with Fritz AI

Image Segmentation

• Embrace your new look with Hair Segmentation by Fritz—Now available for Android developers
• Creating a Pet Sticker App on Android with Fritz Pet Segmentation
• Image Segmentation in Android with Fritz AI

Pose Estimation

• Pose Estimation on Android
• Detecting cropped faces in images using the Fritz Pose Estimation API
• Human Pose Estimation in Android using Fritz AI

Text Recognition

• Text Recognition with ML Kit
• Solve WordSearch games with Android and ML Kit
• Extracting Text from Images on Android
• Card Scanner on Android Using Firebase’s ML Kit and CameraX
• Recognize Text in Images on Android with Firebase’s ML Kit
• Identify Language of Text on Android Using Google’s ML Kit

Other

• Working with the OpenCv Camera for Android: Rotating, Orienting, and Scaling
• CameraX: ‘The’ Machine Learning Camera Library for Android
• Automate testing of TensorFlow Lite model implementation
• Running Artificial Neural Networks in Android using OpenCV
• On-Device Activity Recognition
• Video Processing in Android with Fritz AI
• Fritz AI in Flutter Applications
• Scan Barcodes on Android Using the Power of ML Kit and Fotoapparat
• A Definitive Guide for Audio Processing in Android with TensorFlow Lite Models
• Q-Learning With The Frozen Lake Environment In Android

Natural Language Processing

• Implementing ML Kit’s Smart Reply API in an Android App
• How to Code Natural Language Processing on Android with IBM Watson
• Machine Learning in Action: Building a Universal Translator App for Android with Kotlin
• Creating an offline translation Android app using Firebase ML Kit
• Enhancing Word Suggestions for Auto-completing Text in Android

Model Conversion/Deployment/Management

• Intro to Machine Learning on Android Part 1—How to convert a custom model to TensorFlow Lite
• Deploying PyTorch and Kera Models to Android with TensorFlow Mobile
• Compiling a TensorFlow Lite Build with Custom Operations
• Benchmarking TensorFlow Mobile on Android devices in production
• Profiling TensorFlow Lite Models for Android
• Exploring Firebase ML Kit on Android: Introducing ML Kit (Part 1)
• Deploying Keras Deep Learning Models with Java
• Exporting TensorFlow models to ML Kit
• From Keras to ML Kit
• Using TensorFlow Lite on Android
• Using Deep Learning and Neural Networks in Android Applications
• Loading and running a quantized TensorFlow Lite model on Android
• Coding Feed-Forward Neural Networks in Kotlin (or Android)

Cross/Multi-Platform and IoT/Edge

Mobile

• Machine Learning models on the edge: mobile and Iot
• Troubleshooting TensorFlow Lite on Windows 10
• The Mobile Neural Network Lottery
• Transfer learning: Can it enable AI on every smartphone?
• Building an Image Recognition Model for Mobile using Depthwise Convolutions
• 8-bit Quantization and TensorFlow Lite: Speeding up mobile inference with low precision
• Neural Networks on Mobile Devices with TensorFlow Lite: A Tutorial
• Building Text Detection apps for iOS and Android using React-Native
• Using ONNX to Transfer Machine Learning Models from PyTorch to Caffe2 and Mobile
• Hardware acceleration for machine learning on Apple and Android devices
• 20-Minute Masterpiece: Training your own mobile-ready style transfer model
• Comparing Firebase ML Kit’s Text Recognition on Android & iOS
• Creating a 17 KB style transfer model with layer pruning and quantization
• Leveraging AI with Location Data in Mobile Apps
• Exploring the MobileNet Models in TensorFlow
• Distributing on-device machine learning models with tags and metadata
• Real-Time 2D/3D Feature Point Extraction from a Mobile Camera
• Using Generative Deep Learning Models On-Device
• Using Generative Deep Learning Models On-Device, Part 2: Text & Audio
• Build and AI-Powered Artistic Style Transfer App with Fritz and React Native
• How to use the Style Transfer API in React Native with Fritz
• Machine Learning and Augmented Reality Combined in One Sleek Mobile App – How We Built CarLens
• Increasing the Accuracy of the Machine Learning Model in the CarLens Mobile App
• How to build custom TensorFlow binary for Android and iOS
• Testing TensorFlow Lite image classification model
• Deploy ML models using Flask as REST API and access via Flutter app
• How to Deploy Machine Learning Models on Mobile and Embedded Devices
• Real-time Mobile Video Object Detection using Tensorflow
• Create AR experiences with the Fritz Unity SDK — Bird Perch Tutorial with Pose Estimation
• Image Classification on React Native with TensorFlow.js and MobileNet
• Training a TensorFlow Lite model for mobile using AutoML Vision Edge
• GPU-Accelerated Mobile Multi-view Style Transfer
• Android Image Classification with TensorFlow Lite & Azure Custom Vision Service
• Text Recognition in Flutter Using Firebase’s ML Kit
• Real-Time 3D Object Detection on Mobile Devices with MediaPipe
• Image Labeling in Flutter Using Firebase’s ML Kit
• Face Detection in Flutter Using Firebase’s ML Kit
• Building a Cross-Platform Image Classifier with Flutter and TensorFlow Lite
• Deep Learning for Natural Language Processing on Mobile Devices
• Scan Barcodes in Flutter Using Firebase’s ML Kit
• Implementing MobileBERT for Next Sentence Prediction
• Training an Image Classification Model for Mobile using TensorFlow Lite
• Generative Adversarial Networks (GANs) for Mobile Devices
• TensorFlow Estimators — TFLite and Model Generation
• Neural Network Pruning Research Review 2020
• Quantization Arithmetic
• MobileBERT Using PyTorch for Multiple Choice
• Practical tips for better quantization results
• TensorFlow Lite Model for On-Device Housing Price Predictions

Edge/Browser

• Using TensorFlow.js to Automate the Chrome Dinosaur Game
• Real-Time Object Detection on Raspberry Pi Using OpenCV DNN
• Building an image recognition app using ONNX.js
• Edge TPU: Hands-On with Google’s Coral USB Accelerator
• Building a Vision-Controlled Car Using Raspberry Pi—From Scratch
• Raspberry Pi and machine learning: How to get started
• Keras and deep learning on the Raspberry Pi
• Accelerating Convolutional Neural Networks on Raspberry Pi
• Machine Learning in Node.js with TensorFlow.js
• How to run a Keras model on Jetson Nano
• Build AI that works offline with Coral Dev Board, Edge TPU, and TensorFlow Lite
• Google Coral Edge TPU vs NVIDIA Jetson Nano: A quick deep dive into EdgeAI performance
• Benchmarking Edge Computing
• Merging TensorFlow Lite and μTensor
• Object detection and image classification with Google Coral USB Accelerator
• Build a Hardware-based Face Recognition System for 0 with the Nvidia Jetson Nano and Python
• A Brief Guide to the Intel Movidius Neural Compute Stick with Raspberry Pi 3
• Coral USB Accelerator, TensorFlow Lite C++ API & Raspberry Pi for Edge TPU object detection
• Portable Computer Vision: TensorFlow 2.0 on a Raspberry Pi (Part 1 of 2)
• TensorFlow Lite Ported to Arduino
• Getting Started with Edge AI Using the GAP8 Processor
• Real-Time Person Tracking on the Edge with a Raspberry Pi
• Building Brains on the Edge
• Using TensorFlow.js to Train a “Rock-Paper-Scissors” Model
• The Big Benchmarking Roundup
• Comprehensive TensorFlow.js Example
• Fruit identification using Arduino and TensorFlow
• AutoML Vision Edge: Build machine learning models for mobile and edge devices—in hours
• Creating a TensorFlow Lite Object Detection Model using Google Cloud AutoML
• AutoML Vision Edge: Exporting and Loading TensorFlow SavedModels with Python
• AutoML Vision Edge: Loading and Running a TensorFlow.js Model (Part 1)
• AutoML Vision Edge: Loading and Running a TensorFlow.js Model (Part 2)
• Hacking Google Coral Edge TPU: motion blur and Lanczos resize
• PhotoBooth Lite on Raspberry Pi with TensorFlow Lite
• AutoML Vision Edge: Deploying and Running TensorFlow Models using Docker Containers
• Machine Learning at the Edge — μML
• Face and hand tracking in the browser with MediaPipe and TensorFlow.js
• AutoML Vision Edge: Comparing Model Formats
• Using Google Cloud AutoML Edge Image Classification Models in Python
• Using Google Cloud AutoML Edge Object Detection Models in Python
• Running TensorFlow Lite Image Classification Models in Python
• Running TensorFlow Lite Object Detection Models in Python
• Make deep learning models run fast on embedded hardware
• Creating “No Trump Social” with TensorFlow.js
• Deep Learning with JavaScript (Part 1)
• Deep Learning in JavaScript (Part 2)
• Deep Learning in JavaScript (Part 3)
• Deep Learning In JavaScript (Part 4)
• Constructing a 3D Face Mesh from Face Landmarks in Real-Time with TensorFlow.js and Plot.js
• Introduction to hand detection in the browser with Handtrack.js and TensorFlow
• How to Detect Face Masks in Images with TensorFlow.js
• Multi-team object detection for football games on Raspberry Pi 3
• Real-Time Human Pose Estimation with TensorFlow.js
• How to detect a “thumbs-up” in the browser with TensorFlow.js
• Getting Started with Face Landmark Detection in the Browser with TensorFlow.JS
• TensorFlow Lite Micro
• Body Segmentation in the Browser with TensorFlow.js
• Getting Started with Object Detection Using TensorFlow.js
• Using the Snapdragon Neural Processing Engine for efficient edge deployment of ML models
• Image Classification Made Easy in the Browser with TensorFlow.js
• End-to-end object detection using EfficientDet on Raspberry Pi 3 (Part 1)
• End-to-end Object Detection Using EfficientDet on Raspberry Pi 3 (Part 2)
• End-to-End Object Detection Using EfficientDet on Raspberry Pi 3 (Part 3)
• Building a “Motivation Bot” with TensorFlow.js, Face Detection, and Emotion Classification
• Getting Started with Image Segmentation Using TensorFlow.js
• Safer and Smarter: Contactless shopping with on-device object detection

Online Courses, Videos, & E-Books

iOS

Courses

• Core ML: Machine Learning for iOS—Udacity
• Fundamentals of Core ML: Machine Learning for iOS—Udemy
• Machine Learning in iOS Using Swift—Udemy
• Complete iOS Machine Learning Masterclass—Udemy
• Machine Learning with Core ML 2 and Swift 5—Udemy

Video Tutorials

• A Guide to Core ML on iOS
• Understand Core ML in 5 Minutes
• Machine Learning tutorial with Core ML 2—Part 1
• Machine Learning tutorial with Core ML 2—Part 2
• iOS 12 Swift Tutorial: Create a Fruit Classifier with Creat ML
• Machine Learning with Core ML in iOS 11: Training Core ML & Using Vision Framework

E-Books

• The Mobile Machine Learning Lifecycle
• Core ML Survival Guide
• Machine Learning by Tutorials
• Machine Learning with Core ML: An iOS developer’s guide to implementing machine learning in mobile apps
• Machine Learning with Swift
• Building Mobile Applications with TensorFlow
• Practical Artificial Intelligence with Swift

Android

Courses

• Mobile Machine Learning for Android: TensorFlow and Python—Udemy
• Machine Learning for Android App Development Using ML Kit—Udemy

Video Tutorials/Talks

• Machine Learning with TensorFlow—On-Device
• A Guide to Running TensorFlow Models on Android
• Android Developer’s Guide to Machine Learning: With ML Kit and TensorFlow Lite

Other

• Building Mobile Applications with TensorFlow
• TinyML Machine Learning with TensorFlow on Arduino, and Ultra-Low Power Micro-Controllers

Publications to Follow

• Heartbeat: Covering the intersection of machine learning and mobile app development.
• ProAndroidDev: Professional Android Development: the latest posts from Android Professionals and Google Developer Experts.
• Flawless App Stories: Community around iOS development, mobile design and marketing
• AppCoda Tutorials: A great collection of Swift and iOS app development tutorials.
• Swift Programming: Tutorials and articles covering various Swift-related topics.
• Analytics Vidhya: Analytics Vidhya is a community of Analytics and Data Science professionals.
• Towards Data Science: A platform for thousands of people to exchange ideas and to expand our understanding of data science.
• FreeCodeCamp: Stories worth reading about programming and technology from an open source community.
• Machine, Think!: Matthijs Hollemans’s blog that features deep dives on topics related to deep learning on iOS.
• Pete Warden’s Blog: Pete Warden is the CTO of Jetpac and writes about a variety of ML topics, including frequent looks at issues in mobile/edge ML.
• Machine Learning Mastery: Jason Brownlee's library of quick-start guides, tutorials, and e-books, all designed to help developers learn machine learning.
• Think, mobile!: Mirek Stanek's excellent blog covering a range of topics on mobile intelligence.

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Heartbeat is a community of developers interested in the intersection of mobile and machine learning. Chat with us in Slack, and stay up to date on industry news, trends, and more by subscribing to Deep Learning Weekly.

 

[출처] https://github.com/fritzlabs/Awesome-Mobile-Machine-Learning