├── .gitignore ├── LICENSE └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Xcode 2 | # 3 | # gitignore contributors: remember to update Global/Xcode.gitignore, Objective-C.gitignore & Swift.gitignore 4 | 5 | ## Build generated 6 | build/ 7 | DerivedData/ 8 | 9 | ## Various settings 10 | *.pbxuser 11 | !default.pbxuser 12 | *.mode1v3 13 | !default.mode1v3 14 | *.mode2v3 15 | !default.mode2v3 16 | *.perspectivev3 17 | !default.perspectivev3 18 | xcuserdata/ 19 | 20 | ## Other 21 | *.moved-aside 22 | *.xccheckout 23 | *.xcscmblueprint 24 | 25 | ## Obj-C/Swift specific 26 | *.hmap 27 | *.ipa 28 | *.dSYM.zip 29 | *.dSYM 30 | 31 | ## Playgrounds 32 | timeline.xctimeline 33 | playground.xcworkspace 34 | 35 | # Swift Package Manager 36 | # 37 | # Add this line if you want to avoid checking in source code from Swift Package Manager dependencies. 38 | # Packages/ 39 | # Package.pins 40 | .build/ 41 | 42 | # CocoaPods 43 | # 44 | # We recommend against adding the Pods directory to your .gitignore. However 45 | # you should judge for yourself, the pros and cons are mentioned at: 46 | # https://guides.cocoapods.org/using/using-cocoapods.html#should-i-check-the-pods-directory-into-source-control 47 | # 48 | # Pods/ 49 | 50 | # Carthage 51 | # 52 | # Add this line if you want to avoid checking in source code from Carthage dependencies. 53 | # Carthage/Checkouts 54 | 55 | Carthage/Build 56 | 57 | # fastlane 58 | # 59 | # It is recommended to not store the screenshots in the git repo. Instead, use fastlane to re-generate the 60 | # screenshots whenever they are needed. 61 | # For more information about the recommended setup visit: 62 | # https://docs.fastlane.tools/best-practices/source-control/#source-control 63 | 64 | fastlane/report.xml 65 | fastlane/Preview.html 66 | fastlane/screenshots 67 | fastlane/test_output 68 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # awesome-machine-learning [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 2 | 3 | ❤️ Support my apps ❤️ 4 | 5 | - [Push Hero - pure Swift native macOS application to test push notifications](https://onmyway133.com/pushhero) 6 | - [PastePal - Pasteboard, note and shortcut manager](https://onmyway133.com/pastepal) 7 | - [Quick Check - smart todo manager](https://onmyway133.com/quickcheck) 8 | - [Alias - App and file shortcut manager](https://onmyway133.com/alias) 9 | - [My other apps](https://onmyway133.com/apps/) 10 | 11 | ❤️❤️😇😍🤘❤️❤️ 12 | 13 | I like to explore machine learning, but don't want the to dive into other platforms, like Python or Javascript, to understand some frameworks, or TensorFlow. Luckily, at WWDC 2017, Apple introduces Core ML, Vision, ARKit, which makes working with machine learning so much easier. With all the pre-trained models, we can build great things. It's good to feel the outcome first, then try to explore advanced topics and underlying mechanisms 🤖 14 | 15 | This will curates things mostly related to Core ML, and Swift. There are related things in other platforms if you want to get some references 16 | 17 | ## Table of contents 18 | 19 | - [Core ML](#core-ml) 20 | - [TensorFlow](#tensorflow) 21 | - [Keras](#keras) 22 | - [Turi Create](#turi-create) 23 | - [Machine Learning](#machine-learning) 24 | - [Misc](#misc) 25 | 26 | ## Core ML 27 | 28 | ### Models :rocket: 29 | 30 | - [Awesome-CoreML-Models](https://github.com/likedan/Awesome-CoreML-Models) Largest list of models for Core ML (for iOS 11+) 31 | - [caffe](https://github.com/BVLC/caffe) Caffe: a fast open framework for deep learning. http://caffe.berkeleyvision.org/ 32 | - [deep-learning-models](https://github.com/fchollet/deep-learning-models) Keras code and weights files for popular deep learning models. 33 | - [tensorflow models](https://github.com/tensorflow/models) Models built with TensorFlow 34 | - [libSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) A Library for Support Vector Machines 35 | - [scikit-learn](http://scikit-learn.org/) Machine Learning in Python 36 | - [xgboost](https://github.com/dmlc/xgboost) Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow 37 | - [Keras-Classification-Models](https://github.com/titu1994/Keras-Classification-Models) Collection of Keras models used for classification 38 | - [MobileNet-Caffe](https://github.com/shicai/MobileNet-Caffe) Caffe Implementation of Google's MobileNets 39 | - [ModelZoo](https://github.com/cocoa-ai/ModelZoo) A central GitHub repository for sharing Core ML models 40 | - [StyleArt](https://github.com/ileafsolutions/StyleArt) Style Art library process images using COREML with a set of pre trained machine learning models and convert them to Art style 41 | - [models](https://github.com/Sarasra/models) Models and examples built with TensorFlow 42 | - [Core ML Store](https://coreml.store/) 43 | 44 | ### Tools 45 | 46 | - [coremltools](https://pypi.python.org/pypi/coremltools) coremltools in a python package for creating, examining, and testing models in the .mlmodel format 47 | - [torch2coreml](https://github.com/prisma-ai/torch2coreml) This tool helps convert Torch7 models into Apple CoreML 48 | - [turicreate](https://github.com/apple/turicreate) Turi Create simplifies the development of custom machine learning models. 49 | - [Netron](https://github.com/lutzroeder/Netron) Viewer for neural network and machine learning models 50 | - [onnx-coreml](https://github.com/onnx/onnx-coreml) ONNX to CoreML Converter 51 | - [tf-coreml](https://github.com/tf-coreml/tf-coreml) TensorFlow to CoreML Converter 52 | - [tensorwatch](https://github.com/microsoft/tensorwatch) Debugging and visualization tool for machine learning and data science 53 | 54 | ### Posts 55 | 56 | - [Swift Tutorial: Native Machine Learning and Machine Vision in iOS 11](https://hackernoon.com/swift-tutorial-native-machine-learning-and-machine-vision-in-ios-11-11e1e88aa397) 57 | - [How to train your own model for CoreML](http://reza.codes/2017-07-29/how-to-train-your-own-dataset-for-coreml/) 58 | - [Smart Gesture Recognition in iOS 11 with Core ML and TensorFlow](https://hackernoon.com/smart-gesture-recognition-in-ios-11-with-core-ml-and-tensorflow-1a0a92c99c51) 59 | - [DIY Prisma app with CoreML](https://blog.prismalabs.ai/diy-prisma-app-with-coreml-6b4994cc99e1) :rocket: 60 | - [Using scikit-learn and CoreML to Create a Music Recommendation Engine](https://www.agnosticdev.com/blog-entry/python/using-scikit-learn-and-coreml-create-music-recommendation-engine) 61 | - [Building Not Hotdog with Turi Create and Core ML — in an afternoon](https://heartbeat.fritz.ai/building-not-hotdog-with-turi-create-and-core-ml-in-an-afternoon-a87fd1967d10) 62 | - [Build a Taylor Swift detector with the TensorFlow Object Detection API, ML Engine, and Swift](https://towardsdatascience.com/build-a-taylor-swift-detector-with-the-tensorflow-object-detection-api-ml-engine-and-swift-82707f5b4a56) 63 | - [Leveraging Machine Learning in iOS For Improved Accessibility](https://medium.com/buffer-engineering/leveraging-machine-learning-in-ios-for-improved-accessibility-fc7796c5326f) 64 | - [IBM Watson Services for Core ML Tutorial](https://www.raywenderlich.com/190191/ibm-watson-services-for-core-ml-tutorial) 65 | - [Beginning Machine Learning with Keras & Core ML](https://www.raywenderlich.com/181760/beginning-machine-learning-keras-core-ml) 66 | - [Detecting Whisky brands with Core ML and IBM Watson services](https://martinmitrevski.com/2018/04/14/detecting-whisky-brands-with-core-ml-and-ibm-watson-services/) 67 | - [Detecting Avengers superheroes in your iOS app with IBM Watson and CoreML](https://medium.com/flawless-app-stories/detecting-avengers-superheroes-in-your-ios-app-with-ibm-watson-and-coreml-fe38e493a4d1) 68 | - [Machine Learning](https://developer.apple.com/machine-learning/) Build more intelligent apps with machine learning. 69 | - [Apple Machine Learning Journal](https://machinelearning.apple.com/) 70 | - [Introducing Core ML](https://developer.apple.com/videos/play/wwdc2017/703/) 71 | - [Core ML in depth](https://developer.apple.com/videos/play/wwdc2017/710/) 72 | - [Core ML and Vision: Machine Learning in iOS 11 Tutorial](https://www.raywenderlich.com/164213/coreml-and-vision-machine-learning-in-ios-11-tutorial) 73 | - [iOS 11: Machine Learning for everyone](http://machinethink.net/blog/ios-11-machine-learning-for-everyone/) 74 | - [Everything a Swift Dev Ever Wanted to Know About Machine Learning](https://news.realm.io/news/swift-developer-on-machine-learning-try-swift-2017-gallagher/) 75 | - [Bringing machine learning to your iOS apps](https://news.realm.io/news/altconf-2017-meghan-kane-bringing-machine-learning-to-your-ios-apps/) 76 | - [Pros and cons of iOS machine learning APIs](http://machinethink.net/blog/machine-learning-apis/) :rocket: 77 | - [Core ML: Machine Learning for iOS](https://www.udacity.com/course/core-ml--ud1038) :rocket: 78 | - [Bootstrapping the Machine Learning Training Process](https://www.youtube.com/watch?v=ugiPfm8ICZo) 79 | - [Detecting Pneumonia in an iOS App with Create ML](https://heartbeat.fritz.ai/detecting-pneumonia-in-an-ios-app-with-create-ml-5cff2a60a3d) 80 | - [How to fine-tune ResNet in Keras and use it in an iOS App via Core ML](https://heartbeat.fritz.ai/how-to-fine-tune-resnet-in-keras-and-use-it-in-an-ios-app-via-core-ml-ee7fd84c1b26) 81 | - [Five Common Data Quality Gotchas in Machine Learning and How to Detect Them Quickly](https://doordash.engineering/2022/09/27/five-common-data-quality-gotchas-in-machine-learning-and-how-to-detect-them-quickly/) 82 | 83 | ### Repos 84 | 85 | - [Core-ML-Sample](https://github.com/yulingtianxia/Core-ML-Sample) A Demo using Core ML Framework 86 | - [UnsplashExplorer-CoreML](https://github.com/ahmetws/UnsplashExplorer-CoreML) Core ML demo app with Unsplash API 87 | - [MNIST_DRAW](https://github.com/hwchong/MNIST_DRAW) This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference. 88 | - [CocoaAI](https://github.com/cocoa-ai/CocoaAI) The Cocoa Artificial Intelligence Lab :rocket: 89 | - [complex-gestures-demo](https://github.com/mitochrome/complex-gestures-demo) A demonstration of using machine learning to recognize 13 complex gestures in an iOS app 90 | - [Core-ML-Car-Recognition](https://github.com/likedan/Core-ML-Car-Recognition) A Car Recognition Framework for CoreML 91 | - [CoreML-in-ARKit](https://github.com/hanleyweng/CoreML-in-ARKit) Simple project to detect objects and display 3D labels above them in AR. This serves as a basic template for an ARKit project to use CoreML 92 | - [trainer-mac](https://github.com/mortenjust/trainer-mac) Trains a model, then generates a complete Xcode project that uses it - no code necessary 93 | - [GestureAI-CoreML-iOS](https://github.com/akimach/GestureAI-CoreML-iOS) Hand-gesture recognition on iOS app using CoreML 94 | - [visual-recognition-coreml](https://github.com/watson-developer-cloud/visual-recognition-coreml) Classify images offline using Watson Visual Recognition and Core ML 95 | 96 | ## TensorFlow 97 | 98 | ### Posts 99 | 100 | - [Open Source TensorFlow Models (Google I/O '17)](https://www.youtube.com/watch?v=9ziVGkt8Gg4) 101 | - [Swift for TensorFlow](https://www.youtube.com/watch?v=Yze693W4MaU) 102 | - [Get started with TensorFlow high-level APIs (Google I/O '18)](https://www.youtube.com/watch?v=tjsHSIG8I08&list=PLOU2XLYxmsIInFRc3M44HUTQc3b_YJ4-Y&index=37) 103 | - [Getting started with TensorFlow on iOS](http://machinethink.net/blog/tensorflow-on-ios/) 104 | - [Introducing TensorFlow.js: Machine Learning in Javascript](https://medium.com/tensorflow/introducing-tensorflow-js-machine-learning-in-javascript-bf3eab376db) 105 | - [TensorFlow for JavaScript (Google I/O '18)](https://www.youtube.com/watch?v=OmofOvMApTU) 106 | - [Colab](https://colab.research.google.com) Colaboratory is a Google research project created to help disseminate machine learning education and research 107 | - [Use TensorFlow and BNNS to Add Machine Learning to your Mac or iOS App](https://www.bignerdranch.com/blog/use-tensorflow-and-bnns-to-add-machine-learning-to-your-mac-or-ios-app/) 108 | 109 | ### Repos 110 | 111 | - [workshops](https://github.com/tensorflow/workshops) A few exercises for use at events Google IO 2018 112 | 113 | ### Courses 114 | 115 | - [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) 116 | - [Deep Learning with Python by Francois Chollet](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438) 117 | - [Learn from ML experts at Google](https://ai.google/education) 118 | 119 | ## Keras 120 | 121 | ### Posts 122 | 123 | - [Running Keras models on iOS with CoreML](https://www.pyimagesearch.com/2018/04/23/running-keras-models-on-ios-with-coreml/) 124 | - [Keras and Convolutional Neural Networks (CNNs)](https://www.pyimagesearch.com/2018/04/16/keras-and-convolutional-neural-networks-cnns/) 125 | - [Keras Tutorial : Transfer Learning using pre-trained models]()https://www.learnopencv.com/keras-tutorial-transfer-learning-using-pre-trained-models/ 126 | 127 | ## Turi Create 128 | 129 | ### Posts 130 | 131 | - [Building Not Hotdog with Turi Create and Core ML — in an afternoon](https://hackernoon.com/building-not-hotdog-with-turi-create-and-core-ml-in-an-afternoon-231b14738edf) 132 | - [Natural Language Processing on iOS with Turi Create](https://www.raywenderlich.com/185515/natural-language-processing-on-ios-with-turi-create) 133 | - [Machine Learning in iOS: Turi Create and CoreML](https://medium.com/flawless-app-stories/machine-learning-in-ios-turi-create-and-coreml-5ddce0dc8e26) 134 | - [A Guide to Turi Create](https://developer.apple.com/videos/play/wwdc2018/712/) 135 | 136 | ## Machine Learning 137 | 138 | ### Getting started 139 | 140 | - [A Thank You note to Towards Data Science](https://towardsdatascience.com/a-thank-you-note-to-towards-data-science-58b714a824f8) 141 | - [This is why anyone can learn Machine Learning](https://medium.freecodecamp.org/this-is-why-anyone-can-learn-machine-learning-a5333ee64dff) 142 | 143 | ### Posts 144 | 145 | - [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) 146 | - [Machine Learning is Fun!](https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471) 147 | - [10 Machine Learning Terms Explained in Simple English](http://blog.aylien.com/10-machine-learning-terms-explained-in-simple/) 148 | - [Machine Learning in a Year](https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c) 149 | - [Machine Learning Self-study Resources](https://ragle.sanukcode.net/articles/machine-learning-self-study-resources/) 150 | - [How to Learn Machine Learning](https://elitedatascience.com/learn-machine-learning) 151 | - [Getting Started with Machine Learning](https://medium.com/@suffiyanz/getting-started-with-machine-learning-f15df1c283ea) 152 | - [The Non-Technical Guide to Machine Learning & Artificial Intelligence](https://machinelearnings.co/a-humans-guide-to-machine-learning-e179f43b67a0) 153 | - [Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms](http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/) 154 | - [A Tour of Machine Learning Algorithms](http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/) 155 | - [Machine Learning for Hackers](https://www.youtube.com/playlist?list=PL2-dafEMk2A4ut2pyv0fSIXqOzXtBGkLj) 156 | - [Machine Learning for Developers For absolute beginners and fifth graders](https://xyclade.github.io/MachineLearning/) 157 | - [dive-into-machine-learning](https://github.com/hangtwenty/dive-into-machine-learning) Dive into Machine Learning with Python Jupyter notebook and scikit-learn 158 | - [An introduction to machine learning with scikit-learn](http://scikit-learn.org/stable/tutorial/basic/tutorial.html) 159 | - [Building powerful image classification models using very little data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) 160 | - [How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Nativ](https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3) 161 | - [Hello World - Machine Learning Recipes #1](https://www.youtube.com/watch?v=cKxRvEZd3Mw) 162 | - [An Intuitive Explanation of Convolutional Neural Networks](https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/) :rocket: 163 | - [A Quick Introduction to Neural Networks](https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/) 164 | - [Machine Learning Algorithm for Flappy Bird using Neural Network and Genetic Algorithm](http://www.askforgametask.com/tutorial/machine-learning-algorithm-flappy-bird/) 165 | - [Understanding How Machines Learn, Through Prototyping](https://medium.com/bigtomorrow/understanding-how-machines-learn-through-prototyping-9bdaa3ce7baa#.mura2rwy2) :rocket: 166 | - [Training on the device](http://machinethink.net/blog/training-on-device/) 167 | - [Machine Learning for iOS](https://www.invasivecode.com/weblog/machine-learning-swift-ios/) 168 | - [The “hello world” of neural networks](http://machinethink.net/blog/the-hello-world-of-neural-networks/) 169 | - [Convolutional Neural Networks in iOS 10 and macOS](https://www.invasivecode.com/weblog/convolutional-neural-networks-ios-10-macos-sierra/) 170 | - [LearningMachineLearning](https://github.com/graceavery/LearningMachineLearning) Swift implementation of "Data Science From Scratch" and http://karpathy.github.io/neuralnets/ 171 | - [The “hello world” of neural networks](http://machinethink.net/blog/the-hello-world-of-neural-networks/) 👶 172 | - [EmojiIntelligence](https://github.com/Luubra/EmojiIntelligence) Neural Network built in Apple Playground using Swift 👶 173 | - [Machine Learning: End-to-end Classification](https://www.raywenderlich.com/5554-machine-learning-end-to-end-classification) 174 | - [Machine Learning Zero to Hero (Google I/O'19)](https://www.youtube.com/watch?v=VwVg9jCtqaU&t=315s) :rocket: 175 | 176 | 177 | ### Convolution neural network 178 | 179 | - [CS231n Winter 2016 Lecture 7 Convolutional Neural Networks](https://www.youtube.com/watch?v=AQirPKrAyDg) 180 | - [How to build your own Neural Network from scratch in Python](https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6) :star: 181 | - [How Convolutional Neural Networks work](https://www.youtube.com/watch?v=FmpDIaiMIeA) :star: 182 | 183 | ## Misc 184 | 185 | ### Blogs 186 | 187 | - [Fritz Heartbeat](https://heartbeat.fritz.ai/) :star: 188 | 189 | ### Create ML 190 | 191 | - [Introduction to Create ML: How to Train Your Own Machine Learning Model in Xcode 10](https://www.appcoda.com/create-ml/) 192 | 193 | ### ML Kit 194 | 195 | - [Introducing ML Kit](https://developers.googleblog.com/2018/05/introducing-ml-kit.html) 196 | 197 | ### Vision 198 | 199 | - [Vision](https://developer.apple.com/documentation/vision) Apply high-performance image analysis and computer vision techniques to identify faces, detect features, and classify scenes in images and video. 200 | - [Blog-Getting-Started-with-Vision](https://github.com/jeffreybergier/Blog-Getting-Started-with-Vision) 201 | - [Swift World: What’s new in iOS 11 — Vision](https://medium.com/compileswift/swift-world-whats-new-in-ios-11-vision-456ba4156bad) 202 | 203 | ### Natural Language Processing 204 | 205 | - [NSLinguisticTagger](https://developer.apple.com/documentation/foundation/nslinguistictagger) Analyze natural language to tag part of speech and lexical class, identify proper names, perform lemmatization, and determine the language and script (orthography) of text. 206 | - [Linguistic Tagging](https://www.objc.io/issues/7-foundation/linguistic-tagging/) 207 | - [NSLinguisticTagger on NSHipster](http://nshipster.com/nslinguistictagger/) 208 | - [CoreLinguistics](https://github.com/rxwei/CoreLinguistics) This repository contains some fundamental data structures for NLP. 209 | - [SwiftVerbalExpressions](https://github.com/VerbalExpressions/SwiftVerbalExpressions) Swift Port of VerbalExpressions 210 | 211 | ### Metal 212 | 213 | - [Metal](https://developer.apple.com/metal/) 214 | - [MPSCNNHelloWorld: Simple Digit Detection Convolution Neural Networks (CNN)](https://developer.apple.com/library/content/samplecode/MPSCNNHelloWorld/Introduction/Intro.html) 215 | - [MetalImageRecognition: Performing Image Recognition](https://developer.apple.com/library/content/samplecode/MetalImageRecognition/Introduction/Intro.html) 216 | - [Apple’s deep learning frameworks: BNNS vs. Metal CNN](http://machinethink.net/blog/apple-deep-learning-bnns-versus-metal-cnn/) 217 | - [Forge](https://github.com/hollance/Forge) A neural network toolkit for Metal 218 | 219 | ### GamePlayKit 220 | 221 | - [GamePlayKit](https://developer.apple.com/documentation/gameplaykit) 222 | - [Project 34: Four in a Row](https://www.hackingwithswift.com/read/34/overview) 223 | - [GKMinmaxStrategist: What does it take to build a TicTacToe AI?](http://tilemapkit.com/2015/07/gkminmaxstrategist-build-tictactoe-ai/) 224 | - [GameplayKit Tutorial: Artificial Intelligence](https://www.raywenderlich.com/146407/gameplaykit-tutorial-artificial-intelligence) 225 | - [Gems of GameplayKit](https://vimeo.com/album/4786409/video/235143936) 226 | - [GameplayKit: Beyond Games](https://academy.realm.io/posts/sash-zats-gameplaykit-beyond-games/) 227 | 228 | ### Courses 229 | 230 | - [6.S191: Introduction to Deep Learning](http://introtodeeplearning.com/index.html) 231 | - [Machine Learning](http://introtodeeplearning.com/index.html) 232 | - [Introduction - Intro to Machine Learning on Udacity](https://www.youtube.com/watch?v=ICKBWIkfeJ8&list=PLAwxTw4SYaPkQXg8TkVdIvYv4HfLG7SiH) 233 | - [Machine Learning & Deep Learning Fundamentals by deeplizard](https://www.youtube.com/watch?v=hfK_dvC-avg&list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU) :star: :star: 234 | 235 | ### Interview 236 | 237 | - [41 Essential Machine Learning Interview Questions](https://www.springboard.com/blog/machine-learning-interview-questions/) 238 | 239 | ### Other ML frameworks 240 | 241 | - [TensorSwift](https://github.com/qoncept/TensorSwift) A lightweight library to calculate tensors in Swift, which has similar APIs to TensorFlow's 242 | - [Swift-AI](https://github.com/Swift-AI/Swift-AI) The Swift machine learning library. 243 | - [Swift-Brain](https://github.com/vlall/Swift-Brain) Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI. 244 | - [Bender](https://github.com/xmartlabs/Bender) Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood. 245 | - [BrainCore](https://github.com/aleph7/BrainCore) The iOS and OS X neural network framework 246 | - [AIToolbox](https://github.com/KevinCoble/AIToolbox) A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms 247 | - [brain](https://github.com/harthur/brain) Neural networks in JavaScript 248 | - [TensorFlow](https://www.tensorflow.org/get_started/mnist/beginners) An open-source software library for Machine Intelligence 249 | - [incubator-predictionio](https://github.com/apache/incubator-predictionio) PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray. 250 | - [Caffe](http://caffe.berkeleyvision.org/) Deep learning framework by BAIR 251 | - [Torch](http://torch.ch/) A SCIENTIFIC COMPUTING FRAMEWORK FOR LUAJIT 252 | - [Theano](http://www.deeplearning.net/software/theano/) Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently 253 | - [CNTK](https://github.com/Microsoft/CNTK) Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit 254 | - [MXNet](http://mxnet.io/) Lightweight, Portable, Flexible Distributed/Mobile Deep Learning 255 | 256 | ### Accelerate 257 | 258 | - [Accelerate-in-Swift](https://github.com/hyperjeff/Accelerate-in-Swift) Swift example codes for the Accelerate.framework 259 | - [Surge](https://github.com/mattt/Surge) A Swift library that uses the Accelerate framework to provide high-performance functions for matrix math, digital signal processing, and image manipulation. 260 | 261 | ### Statistics 262 | 263 | - [SigmaSwiftStatistics](https://github.com/evgenyneu/SigmaSwiftStatistics) A collection of functions for statistical calculation written in Swift 264 | 265 | ### Services 266 | 267 | - [Watson](https://www.ibm.com/watson/developercloud/) Enable cognitive computing features in your app using IBM Watson's Language, Vision, Speech and Data APIs. 268 | - [wit.ai](https://wit.ai/) Natural Language for Developers 269 | - [Cloud Machine Learning Engine](https://cloud.google.com/ml-engine/) Machine Learning on any data, any size 270 | - [Cloud Vision API](https://cloud.google.com/vision/) Derive insight from images with our powerful Cloud Vision API 271 | - [Amazon Machine Learning](https://aws.amazon.com/documentation/machine-learning/) Amazon Machine Learning makes it easy for developers to build smart applications, including applications for fraud detection, demand forecasting, targeted marketing, and click prediction 272 | - [api.ai](https://api.ai/) Build brand-unique, natural language interactions for bots, applications, services, and devices. 273 | - [clarifai](https://developer.clarifai.com/) Build amazing apps with the world’s best image and video recognition API. 274 | - [openml](https://www.openml.org/) Exploring machine learning together 275 | - [Lobe](https://lobe.ai/) Deep learning made simple 276 | - [Comparing Machine Learning (ML) Services from Various Cloud ML Service Providers](https://medium.com/@tanyathakur6/comparing-machine-learning-ml-services-from-various-cloud-ml-service-providers-63c8a2626cb6) 277 | 278 | ### Text Recognition 279 | 280 | - [Tesseract OCR Tutorial](https://www.raywenderlich.com/93276/implementing-tesseract-ocr-ios) 281 | - [Tesseract-OCR-iOS](https://github.com/gali8/Tesseract-OCR-iOS) Tesseract OCR iOS is a Framework for iOS7+, compiled also for armv7s and arm64. 282 | - [tesseract.js](https://github.com/naptha/tesseract.js) Pure Javascript OCR for 62 Languages 283 | 284 | ### Speech Recognition 285 | 286 | - [Speech](https://developer.apple.com/documentation/speech) 287 | - [Using the Speech Recognition API in iOS 10](https://code.tutsplus.com/tutorials/using-the-speech-recognition-api-in-ios-10--cms-28032) 288 | - [Speech Recognition Tutorial for iOS](https://www.raywenderlich.com/155752/speech-recognition-tutorial-ios) 289 | - [CeedVocal](https://github.com/creaceed/CeedVocal) Speech Recognition Library for iOS 290 | 291 | ### Speech Synthesizer 292 | 293 | - [AVSpeechSynthesizer](https://developer.apple.com/documentation/avfoundation/avspeechsynthesizer) An object that produces synthesized speech from text utterances and provides controls for monitoring or controlling ongoing speech. 294 | 295 | ### Artificial Intelligence 296 | 297 | - [The classic ELIZA chat bot in Swift.](https://gist.github.com/hollance/be70d0d7952066cb3160d36f33e5636f) 298 | - [Introduction to AI Programming for Games](https://www.raywenderlich.com/24824/introduction-to-ai-programming-for-games) 299 | 300 | ### Google Cloud Platform for Machine Learning 301 | 302 | - [Machine Learning](https://cloud.google.com/products/machine-learning/) 303 | - [Machine Learning APIs by Example (Google I/O '17)](https://www.youtube.com/watch?v=ETeeSYMGZn0) 304 | - [Adding Computer Vision to your iOS App](https://medium.com/@srobtweets/adding-computer-vision-to-your-ios-app-66d6f540cdd2) 305 | 306 | ### Others 307 | 308 | - [NotHotdog-Classifier](https://github.com/kmather73/NotHotdog-Classifier) What would you say if I told you there is a app on the market that tell you if you have a hotdog or not a hotdog. 309 | - [How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native](https://hackernoon.com/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3) 310 | --------------------------------------------------------------------------------