移动端机器学习资源合集

【导读】移动机器学习应用变得越来越广泛。Fritz在Github整理了关于移动端机器学习的论文、图书、代码等资源，值得收藏！

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

开启

(Mostly) 移动机器学习书籍

• 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

• 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

 从数据科学和机器学习开始

• 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.

 移动机器学习框架

 Mobile-Ready

• Fritz: Fritz is the machine learning platform for iOS and Android developers. Teach your mobile apps 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.

• 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

代码，库，资源

 iOS

• 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.

 Android

• 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.

 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 Community Resources

• Mobile ML GitHub Repositories: List of repos with machine learning models ready for mobile, organized by feature type.

• 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

 iOS

 计算机视觉

• 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

• Real-Time Style Transfer for iOS

• 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

• Build your own Portrait Mode on iOS using machine learning in < 30 minutes

• 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

• Pose Estimation on iOS

• Hand Detection with Core ML and ARKit

• Creating a Prisma-like App with Core ML, Style Transfer, and Turi Create

• Integrating Google ML Kit in iOS for Face Detection, Text Recognition, and Many More

• Using Vision Framework for Text Detection in iOS 11

• Introduction to Core ML: Building a Simple Image Recognition App

• MobileNetV2 + SSDLite with Core ML (Object Detection)

• Vision in iOS: Text detection and Tesseract recognition

• 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

• Detecting Whisky brands with Core ML and IBM Watson services

 自然语言处理

• Implementing a Natural Language Classifier in iOS with Keras + Core ML

• Core ML with GloVe Word Embedding and a Recursive Neural Network

• Natural Language on iOS 12: Customizing tag schemes and named entity recognition

• Training a Core ML Model for Sentiment Analysis

• Using Create ML on iOS to auto-complete forms

• Text Recognition and Translation on iOS Using ML Kit and Google Translate

• Introduction to Natural Language Processing in Swift

• Train a Text Classification Model with Create ML

• Comparing iOS Text Recognition SDKS Using Delta

 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

• Ray Wenderlich iOS Machine Learning Tutorials

Android

Computer Vision

• Intro to Machine Learning on Android Part 1—How to convert a custom model to TensorFlow Lite

• Introduction to Machine Learning on Android Part 2: Building an app to recognize handwritten digits

• 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

• Using TensorFlow Lite and ML Kit to Build a “Pokedex” in Android

• Visual Recognition in Android Using IBM Watson

• Real-Time Style Transfer for Android

• Identifying and Counting Items in Real-Time with Fritz Object Detection for Android

• Pose Estimation on Android

• Image Segmentation for Android—Smart Background Replacement

• Building a Custom Machine Learning Model on Android with TensorFlow Lite

• Exploring Firebase ML Kit on Android: Face Detection (Part 2)

• Exploring Firebase ML Kit on Android: Barcode Scanning (Part 3)

• Exploring Firebase ML Kit on Android: Landmark Detection (Part 4)

• Detecting Pikachu on Android using TensorFlow Object Detection

• Using TensorFlow on Android—step by step code explanation

• Mobile intelligence—TensorFlow Lite Classification on Android

• Creating a Google Lens clone using Firebase ML Kit

• Building a pet monitoring app in Android with machine learning

• How to apply Machine Learning to Android using Fritz

• Text Recognition with ML Kit

• Image Recognition with ML Kit

• Applying TensorFlow in Android in 4 steps

• Working with the OpenCv Camera for Android: Rotating, Orienting, and Scaling

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

Model Conversion/Deployment/Management

• 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

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

• 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

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

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

• 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

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

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.

  
  

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