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Alex Rupérez

Published: 09 May 09:33
I starred fritzlabs/Awesome-Mobile-Machine-Learning at GitHub.
A curated list of awesome mobile machine learning resources for iOS, Android, and edge devices.
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A list of awesome mobile machine learning resources curated by Fritz.

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


  • 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.


  • 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


  • 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-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.


  • 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

Tutorials & Learning


Computer Vision

Natural Language Processing

Model Conversion/Deployment/Management



Computer Vision

Natural Language Processing

Model Conversion/Deployment/Management

Cross/Multi-Platform and IoT/Edge



Online Courses, Videos, & E-Books



Video Tutorials




Video Tutorials/Talks

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.