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https://github.com/mikeroyal/CoreML-Guide

Core ML Guide
https://github.com/mikeroyal/CoreML-Guide

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Core ML Guide

Host: GitHub
URL: https://github.com/mikeroyal/CoreML-Guide
Owner: mikeroyal
Created: 2021-09-05T20:36:07.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2022-02-06T20:31:09.000Z (over 2 years ago)
Last Synced: 2024-05-23T09:19:32.176Z (5 months ago)
Topics: awesome, awesome-coreml, awesome-list, awesome-machine-learning, awesome-readme, awesome-resources, coreml, deep-learning, deep-neural-networks, ios, ios-swift, lists, machine-learning, macos, neural-engine, xcode
Language: Swift
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Open Issues: 0
Metadata Files:
- Readme: README.md

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ultimate-awesome - CoreML-Guide - Core ML Guide. (Other Lists / PowerShell Lists)

README

Core ML Guide

#### A guide covering Core ML including the applications, libraries and tools that will make you a better and more efficient Core ML development.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**

# Table of Contents

1. [Core ML Learning Resources](https://github.com/mikeroyal/CoreML-Guide#core-ml-learning-resources)

2. [Core ML Tools, Libraries, and Frameworks](https://github.com/mikeroyal/CoreML-Guide#core-ml-tools-libraries-and-frameworks)

3. [Apple Silicon](https://github.com/mikeroyal/CoreML-Guide#apple-silicon)

4. [Algorithms](https://github.com/mikeroyal/CoreML-Guide#algorithms)

5. [Machine Learning](https://github.com/mikeroyal/CoreML-Guide#machine-learning)

6. [Computer Vision Development](https://github.com/mikeroyal/CoreML-Guide#computer-vision-development)

7. [Natural Language Processing (NLP) Development](https://github.com/mikeroyal/CoreML-Guide#natural-language-processing-nlp-development)

8. [Metal(API) Development](https://github.com/mikeroyal/CoreML-Guide#metal-api-development)

9. [Swift Development](https://github.com/mikeroyal/CoreML-Guide#swift-development)

10. [Objective-C Development](https://github.com/mikeroyal/CoreML-Guide#objective-c-development)

11. [C/C++ Development](https://github.com/mikeroyal/CoreML-Guide#cc-development)

12. [Python Development](https://github.com/mikeroyal/CoreML-Guide#python-development)

# Core ML Learning Resources
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

[Core ML](https://developer.apple.com/documentation/coreml) is an Apple framework for integrating machine learning models into apps running on Apple devices (including iOS, watchOS, macOS, and tvOS). Core ML introduces a public file format (.mlmodel) for a broad set of ML methods including deep neural networks (both convolutional and recurrent), tree ensembles with boosting, and generalized linear models. Models in this format can be directly integrated into apps through Xcode.

[Introduction to Core ML](https://coremltools.readme.io/docs)

[Integrating a Core ML Model into your App](https://developer.apple.com/documentation/coreml/integrating_a_core_ml_model_into_your_app)

[Core ML Models](https://developer.apple.com/machine-learning/models/)

[Core ML API Reference](https://apple.github.io/coremltools/index.html)

[Core ML Specification](https://apple.github.io/coremltools/mlmodel/index.html)

[Apple Developer Forums for Core ML](https://developer.apple.com/forums/tags/core-ml)

[Top Core ML Courses Online | Udemy](https://www.udemy.com/topic/Core-ML/)

[Top Core ML Courses Online | Coursera](https://www.coursera.org/courses?query=core%20ml)

[IBM Watson Services for Core ML | IBM](https://www.ibm.com/watson/stories/coreml)

[Generate Core ML assets using IBM Maximo Visual Inspection | IBM](https://developer.ibm.com/technologies/iot/tutorials/ibm-maximo-visual-inspection-apple-devices/)

# Core ML Tools, Libraries, and Frameworks

[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

[Core ML tools](https://coremltools.readme.io/) is a project that contains supporting tools for Core ML model conversion, editing, and validation.

[Create ML](https://developer.apple.com/machine-learning/create-ml/) is a tool that provides new ways of training machine learning models on your Mac. It takes the complexity out of model training while producing powerful Core ML models.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[XGBoost](https://xgboost.readthedocs.io/) is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.

[LIBSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

[Apple Vision](https://developer.apple.com/documentation/vision) is a framework that performs face and face landmark detection, text detection, barcode recognition, image registration, and general feature tracking. Vision also allows the use of custom Core ML models for tasks like classification or object detection.

[Xcode](https://developer.apple.com/xcode/) includes everything developers need to create great applications for Mac, iPhone, iPad, Apple TV, and Apple Watch. Xcode provides developers a unified workflow for user interface design, coding, testing, and debugging. Xcode 12 is built as an Universal app that runs 100% natively on Intel-based CPUs and Apple Silicon. It includes a unified macOS SDK that features all the frameworks, compilers, debuggers, and other tools you need to build apps that run natively on Apple Silicon and the Intel x86_64 CPU.

[SwiftUI](https://developer.apple.com/documentation/swiftui) is a user interface toolkit that provides views, controls, and layout structures for declaring your app's user interface. The SwiftUI framework provides event handlers for delivering taps, gestures, and other types of input to your application.

[UIKit](https://developer.apple.com/documentation/uikit) is a framework provides the required infrastructure for your iOS or tvOS apps. It provides the window and view architecture for implementing your interface, the event handling infrastructure for delivering Multi-Touch and other types of input to your app, and the main run loop needed to manage interactions among the user, the system, and your app.

[AppKit](https://developer.apple.com/documentation/appkit) is a graphical user interface toolkit that contains all the objects you need to implement the user interface for a macOS app such as windows, panels, buttons, menus, scrollers, and text fields, and it handles all the details for you as it efficiently draws on the screen, communicates with hardware devices and screen buffers, clears areas of the screen before drawing, and clips views.

[ARKit](https://developer.apple.com/augmented-reality/arkit/) is a set set of software development tools to enable developers to build augmented-reality apps for iOS developed by Apple. The latest version ARKit 3.5 takes advantage of the new LiDAR Scanner and depth sensing system on iPad Pro(2020) to support a new generation of AR apps that use Scene Geometry for enhanced scene understanding and object occlusion.

[RealityKit](https://developer.apple.com/documentation/realitykit) is a framework to implement high-performance 3D simulation and rendering with information provided by the ARKit framework to seamlessly integrate virtual objects into the real world.

[SceneKit](https://developer.apple.com/scenekit/) is a high-level 3D graphics framework that helps you create 3D animated scenes and effects in your iOS apps.

[Instruments](https://help.apple.com/instruments/mac/current/#/dev7b09c84f5) is a powerful and flexible performance-analysis and testing tool that’s part of the Xcode tool set. It’s designed to help you profile your iOS, watchOS, tvOS, and macOS apps, processes, and devices in order to better understand and optimize their behavior and performance.

[Cocoapods](https://cocoapods.org/) is a dependency manager for Swift and Objective-C used in Xcode projects by specifying the dependencies for your project in a simple text file. CocoaPods then recursively resolves dependencies between libraries, fetches source code for all dependencies, and creates and maintains an Xcode workspace to build your project.

[AppCode](https://www.jetbrains.com/objc/) is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages.

# Apple Silicon
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

[Does it ARM? Apps that are reported to support Apple Silicon](https://doesitarm.com)

[Apple Hypervisor](https://developer.apple.com/documentation/hypervisor) is a frameowrk that builds virtualization solutions on top of a lightweight hypervisor, without third-party kernel extensions. Hypervisor provides C APIs so you can interact with virtualization technologies in user space, without writing kernel extensions (KEXTs). As a result, the apps you create using this framework are suitable for distribution on the [Mac App Store](https://www.appstore.com/).

[Apple A-series](https://www.apple.com/) is Apple's 64-bit ARM-based system on a chip (SoC) used in their iPhones and iPads. Though, at WWDC 2020 it was announced that [Apple Silicon](https://developer.apple.com/documentation/apple_silicon) would [transition into Mac laptops](https://www.apple.com/newsroom/2020/06/apple-announces-mac-transition-to-apple-silicon/).

[Apple M1 Chip](https://www.apple.com/mac/m1/) is Apple's first SoC chip designed specifically for their ARM Mac products, it delivers incredible performance(8-core CPU and 8-core GPU), custom technologies, and great power efficiency. The M1 Chip is now availble for [Macbook Pro 13 with M1](https://www.apple.com/macbook-pro-13/), [Macbook Air 13 with M1](https://www.apple.com/macbook-air/), and [Mac Mini with M1](https://www.apple.com/mac-mini/).

[Xcode 12](https://developer.apple.com/xcode/) is built as an Universal app that runs 100% natively on Intel-based CPUs and Apple Silicon. It includes a unified macOS SDK that features all the frameworks, compilers, debuggers, and other tools you need to build apps that run natively on Apple Silicon and the Intel x86_64 CPU.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Universal App Quick Start Program](https://developer.apple.com/programs/universal/)

[Writing ARM64 Code for Apple Platforms](https://developer.apple.com/documentation/xcode/writing_arm64_code_for_apple_platforms)

[Porting Your macOS Apps to Apple Silicon](https://developer.apple.com/documentation/xcode/porting_your_macos_apps_to_apple_silicon)

[Building a Universal macOS Binary](https://developer.apple.com/documentation/xcode/building_a_universal_macos_binary)

[Addressing Architectural Differences in Your macOS Code](https://developer.apple.com/documentation/apple_silicon/addressing_architectural_differences_in_your_macos_code)

[Porting Just-In-Time(JIT) Compilers to Apple Silicon](https://developer.apple.com/documentation/apple_silicon/porting_just-in-time_compilers_to_apple_silicon)

[Porting Your Audio Code to Apple Silicon](https://developer.apple.com/documentation/audiounit/porting_your_audio_code_to_apple_silicon)

[Porting Your Metal Code to Apple Silicon](https://developer.apple.com/documentation/metal/porting_your_metal_code_to_apple_silicon)

[Tuning Your Code’s Performance for Apple Silicon](https://developer.apple.com/documentation/os/workgroups/tuning_your_code_s_performance_for_apple_silicon)

[Learn how Rosetta translates executables and what Rosetta can’t translate](https://developer.apple.com/documentation/apple_silicon/about_the_rosetta_translation_environment)

[Running Your iOS Apps on macOS](https://developer.apple.com/documentation/apple_silicon/running_your_ios_apps_on_macos)

[Adapting iOS Code to Run in the macOS Environment](https://developer.apple.com/documentation/apple_silicon/adapting_ios_code_to_run_in_the_macos_environment)

[Implementing Drivers, System Extensions, and Kexts](https://developer.apple.com/documentation/apple_silicon/implementing_drivers_system_extensions_and_kexts)

[Installing a Custom Kernel Extension](https://developer.apple.com/documentation/apple_silicon/installing_a_custom_kernel_extension)

[Debugging a Custom Kernel Extension](https://developer.apple.com/documentation/apple_silicon/debugging_a_custom_kernel_extension)

# Algorithms
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

[Fuzzy logic](https://www.investopedia.com/terms/f/fuzzy-logic.asp) is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.

**Architecture of a Fuzzy Logic System. Source: [ResearchGate](https://www.researchgate.net/figure/Architecture-of-a-fuzzy-logic-system_fig2_309452475)**

[Support Vector Machine (SVM)](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf) is a supervised machine learning model that uses classification algorithms for two-group classification problems.

**Support Vector Machine (SVM). Source:[OpenClipArt](https://openclipart.org/detail/182977/svm-support-vector-machines)**

[Neural networks](https://www.ibm.com/cloud/learn/neural-networks) are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.

**Deep neural network. Source: [IBM](https://www.ibm.com/cloud/learn/neural-networks)**

[Convolutional Neural Networks (R-CNN)](https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.

**Convolutional Neural Networks. Source:[CS231n](https://cs231n.github.io/convolutional-networks/#conv)**

[Recurrent neural networks (RNNs)](https://www.ibm.com/cloud/learn/recurrent-neural-networks) is a type of artificial neural network which uses sequential data or time series data.

**Recurrent Neural Networks. Source: [Slideteam](https://www.slideteam.net/recurrent-neural-networks-rnns-ppt-powerpoint-presentation-file-templates.html)**

[Multilayer Perceptrons (MLPs)](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron) is multi-layer neural networks composed of multiple layers of [perceptrons](https://en.wikipedia.org/wiki/Perceptron) with a threshold activation.

**Multilayer Perceptrons. Source: [DeepAI](https://deepai.org/machine-learning-glossary-and-terms/multilayer-perceptron)**

[Random forest](https://www.ibm.com/cloud/learn/random-forest) is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.

**Random forest. Source: [wikimedia](https://community.tibco.com/wiki/random-forest-template-tibco-spotfirer-wiki-page)**

[Decision trees](https://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/) are tree-structured models for classification and regression.

***Decision Trees. Source: [CMU](http://www.cs.cmu.edu/~bhiksha/courses/10-601/decisiontrees/)*

[Naive Bayes](https://en.wikipedia.org/wiki/Naive_Bayes_classifier) is a machine learning algorithm that is used solved calssification problems. It's based on applying [Bayes' theorem](https://www.mathsisfun.com/data/bayes-theorem.html) with strong independence assumptions between the features.

**Bayes' theorem. Source:[mathisfun](https://www.mathsisfun.com/data/bayes-theorem.html)**

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

**Machine Learning/Deep Learning Frameworks.**

## Learning Resources for ML

[Machine Learning](https://www.ibm.com/cloud/learn/machine-learning) is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[AWS Training and Certification for Machine Learning (ML) Courses](https://aws.amazon.com/training/learning-paths/machine-learning/)

[Machine Learning Scholarship Program for Microsoft Azure from Udacity](https://www.udacity.com/scholarships/machine-learning-scholarship-microsoft-azure)

[Microsoft Certified: Azure Data Scientist Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-data-scientist)

[Microsoft Certified: Azure AI Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-ai-engineer)

[Azure Machine Learning training and deployment](https://docs.microsoft.com/en-us/azure/devops/pipelines/targets/azure-machine-learning)

[Learning Machine learning and artificial intelligence from Google Cloud Training](https://cloud.google.com/training/machinelearning-ai)

[Machine Learning Crash Course for Google Cloud](https://developers.google.com/machine-learning/crash-course/)

[JupyterLab](https://jupyterlab.readthedocs.io/)

[Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/)

[How to run Jupyter Notebooks in your Azure Machine Learning workspace](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

## ML Frameworks, Libraries, and Tools

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apple CoreML](https://developer.apple.com/documentation/coreml) is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[Apache OpenNLP](https://opennlp.apache.org/) is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like [Named Entity Recognition](https://en.wikipedia.org/wiki/Named-entity_recognition), [Sentence Detection](), [POS(Part-Of-Speech) tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging), [Tokenization](https://en.wikipedia.org/wiki/Tokenization_(data_security)) [Feature extraction](https://en.wikipedia.org/wiki/Feature_extraction), [Chunking](https://en.wikipedia.org/wiki/Chunking_(psychology)), [Parsing](https://en.wikipedia.org/wiki/Parsing), and [Coreference resolution](https://en.wikipedia.org/wiki/Coreference).

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).

[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.

[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

# Computer Vision Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## Computer Vision Learning Resources

[Computer Vision](https://azure.microsoft.com/en-us/overview/what-is-computer-vision/) is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.

[OpenCV Courses](https://opencv.org/courses/)

[Exploring Computer Vision in Microsoft Azure](https://docs.microsoft.com/en-us/learn/paths/explore-computer-vision-microsoft-azure/)

[Top Computer Vision Courses Online | Coursera](https://www.coursera.org/courses?languages=en&query=computer%20vision)

[Top Computer Vision Courses Online | Udemy](https://www.udemy.com/topic/computer-vision/)

[Learn Computer Vision with Online Courses and Lessons | edX](https://www.edx.org/learn/computer-vision)

[Computer Vision and Image Processing Fundamentals | edX](https://www.edx.org/course/computer-vision-and-image-processing-fundamentals)

[Introduction to Computer Vision Courses | Udacity](https://www.udacity.com/course/introduction-to-computer-vision--ud810)

[Computer Vision Nanodegree program | Udacity](https://www.udacity.com/course/computer-vision-nanodegree--nd891)

[Machine Vision Course |MIT Open Courseware ](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2004/)

[Computer Vision Training Courses | NobleProg](https://www.nobleprog.com/computer-vision-training)

[Visual Computing Graduate Program | Stanford Online](https://online.stanford.edu/programs/visual-computing-graduate-program)

## Computer Vision Tools, Libraries, and Frameworks

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Automated Driving Toolbox™](https://www.mathworks.com/products/automated-driving.html) is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.

[LRSLibrary](https://github.com/andrewssobral/lrslibrary) is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.

[Image Processing Toolbox™](https://www.mathworks.com/products/image.html) is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.

[Computer Vision Toolbox™](https://www.mathworks.com/products/computer-vision.html) is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.

[Statistics and Machine Learning Toolbox™](https://www.mathworks.com/products/statistics.html) is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.

[Lidar Toolbox™](https://www.mathworks.com/products/lidar.html) is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.

[Mapping Toolbox™](https://www.mathworks.com/products/mapping.html) is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.

[UAV Toolbox](https://www.mathworks.com/products/uav.html) is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.

[Parallel Computing Toolbox™](https://www.mathworks.com/products/matlab-parallel-server.html) is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.

[Partial Differential Equation Toolbox™](https://www.mathworks.com/products/pde.html) is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.

[ROS Toolbox](https://www.mathworks.com/products/ros.html) is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.

[Robotics Toolbox™](https://www.mathworks.com/products/robotics.html) provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.

[Deep Learning Toolbox™](https://www.mathworks.com/products/deep-learning.html) is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.

[Reinforcement Learning Toolbox™](https://www.mathworks.com/products/reinforcement-learning.html) is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.

[Deep Learning HDL Toolbox™](https://www.mathworks.com/products/deep-learning-hdl.html) is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.

[Model Predictive Control Toolbox™](https://www.mathworks.com/products/model-predictive-control.html) is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.

[Vision HDL Toolbox™](https://www.mathworks.com/products/vision-hdl.html) is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.

[Microsoft AirSim](https://microsoft.github.io/AirSim/lidar.html) is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports [software-in-the-loop simulation](https://www.mathworks.com/help///ecoder/software-in-the-loop-sil-simulation.html) with popular flight controllers such as PX4 & ArduPilot and [hardware-in-loop](https://www.ni.com/en-us/innovations/white-papers/17/what-is-hardware-in-the-loop-.html) with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.

# Natural Language Processing (NLP) Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## NLP Learning Resources

[Natural Language Processing (NLP)](https://www.ibm.com/cloud/learn/natural-language-processing) is a branch of artificial intelligence (AI) focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, and deep learning models.

[Natural Language Processing With Python's NLTK Package](https://realpython.com/nltk-nlp-python/)

[Cognitive Services—APIs for AI Developers | Microsoft Azure](https://azure.microsoft.com/en-us/services/cognitive-services/)

[Artificial Intelligence Services - Amazon Web Services (AWS)](https://aws.amazon.com/machine-learning/ai-services/)

[Google Cloud Natural Language API](https://cloud.google.com/natural-language/docs/reference/rest)

[Top Natural Language Processing Courses Online | Udemy](https://www.udemy.com/topic/natural-language-processing/)

[Introduction to Natural Language Processing (NLP) | Udemy](https://www.udemy.com/course/natural-language-processing/)

[Top Natural Language Processing Courses | Coursera](https://www.coursera.org/courses?=&query=natural%20language%20processing)

[Natural Language Processing | Coursera](https://www.coursera.org/learn/language-processing)

[Natural Language Processing in TensorFlow | Coursera](https://www.coursera.org/learn/natural-language-processing-tensorflow)

[Learn Natural Language Processing with Online Courses and Lessons | edX](https://www.edx.org/learn/natural-language-processing)

[Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight](https://www.pluralsight.com/courses/build-natural-language-processing-solution-microsoft-azure)

[Natural Language Processing (NLP) Training Courses | NobleProg](https://www.nobleprog.com/nlp-training)

[Natural Language Processing with Deep Learning Course | Standford Online](https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning)

[Advanced Natural Language Processing - MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)

[Certified Natural Language Processing Expert Certification | IABAC](https://iabac.org/artificial-intelligence-certification/certified-natural-language-processing-expert/)

[Natural Language Processing Course - Intel](https://software.intel.com/content/www/us/en/develop/training/course-natural-language-processing.html)

## NLP Tools, Libraries, and Frameworks

[Natural Language Toolkit (NLTK)](https://www.nltk.org/) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over [50 corpora and lexical resources](https://nltk.org/nltk_data/) such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

[spaCy](https://spacy.io) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It also features neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT.

[CoreNLP](https://stanfordnlp.github.io/CoreNLP/) is a set of natural language analysis tools written in Java. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.

[NLPnet](https://github.com/erickrf/nlpnet) is a Python library for Natural Language Processing tasks based on neural networks. It performs part-of-speech tagging, semantic role labeling and dependency parsing.

[Flair](https://github.com/flairNLP/flair) is a simple framework for state-of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.

[Catalyst](https://github.com/curiosity-ai/catalyst) is a C# Natural Language Processing library built for speed. Inspired by [spaCy's design](https://spacy.io/), it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.

[Tensorflow_macOS](https://github.com/apple/tensorflow_macos) is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

# Metal (API) Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## Metal Learning Resources
[Metal](https://developer.apple.com/metal/) is a low-level API that provides a platform-optimized, low-overhead API for developing the latest 3D pro applications and amazing games using a rich shading language with tighter integration between graphics and compute programs. To help you do more while managing ever more complex shader code, Metal adds an unparalleled suite of advanced GPU debugging tools to help you realize the full potential of your graphics code.

[Apple Developer Documentation](https://developer.apple.com/documentation)

[MetalKit](https://developer.apple.com/documentation/metalkit/)

[Metal Shading Language Specification](https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf)

[Using Metal Feature Set Tables](https://developer.apple.com/documentation/metal/gpu_features/using_metal_feature_set_tables/)

[Metal Performance Shaders](https://developer.apple.com/documentation/metalperformanceshaders/)

[Optimizing Performance with the GPU Counters Instrument](https://developer.apple.com/documentation/metal/optimizing_performance_with_the_gpu_counters_instrument?language=objc)

[Enabling Frame Capture](https://developer.apple.com/documentation/metal/frame_capture_debugging_tools/enabling_frame_capture?language=objc)

[Reducing the Memory Footprint of Metal Apps](https://developer.apple.com/documentation/metal/reducing_the_memory_footprint_of_metal_apps)

[Metal Developer Tools for Windows](https://developer.apple.com/download/release/)

[Metal Sample code](https://developer.apple.com/metal/sample-code/)

[Metal plugin for TensorFlow](https://developer.apple.com/metal/tensorflow-plugin/)

[Metal Developer discussions](https://developer.apple.com/forums/tags/metal/)

## Metal Tools, Libraries, and Frameworks

[Apple Foundation Framework](https://developer.apple.com/documentation/foundation) is a framework provides a base layer of functionality for apps and frameworks, including data storage and persistence, text processing, date and time calculations, sorting and filtering, and networking. The classes, protocols, and data types defined by Foundation are used throughout the macOS, iOS, watchOS, and tvOS SDKs.

[Apple Core Animation Framework](https://developer.apple.com/documentation/quartzcore) is a graphics rendering and animation infrastructure that provides high frame rates and smooth animations without burdening the CPU and slowing down your app.

[Apple Core Graphics Framework](https://developer.apple.com/documentation/coregraphics)is a framework based on the Quartz advanced drawing engine. It provides low-level, lightweight 2D rendering with unmatched output fidelity.

[Paravirtualized Graphics Framework](https://developer.apple.com/documentation/paravirtualizedgraphics) is a framework that implements hardware-accelerated graphics for macOS running in a virtual machine, hereafter known as the guest. The macOS operating system provides a graphics driver that runs inside the guest, communicating with the framework in the host operating system to take advantage of Metal-accelerated graphics.

[SceneKit](https://developer.apple.com/scenekit/) is a high-level 3D graphics framework that helps you create 3D animated scenes and effects in your iOS apps.

[MoltenVK](https://moltengl.com/moltenvk) is an implementation of Vulkan running on iOS and macOS using Apple's [Metal](https://developer.apple.com/metal/) graphics framework.

# Swift Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

Developing with SwiftUI in Xcode 12

## Swift Learning Resources

[Swift](https://developer.apple.com/swift/) is Apple's main programming language for iOS, macOS, watchOS, and tvOS app development. Though, many parts of Swift will be familiar to developers from their experience of developing in C and Objective-C.

[Swift Evolution](https://github.com/apple/swift-evolution) maintains proposals for changes and user-visible enhancements to the Swift Programming Language.

[Xcode + Swift](https://developer.apple.com/swift/resources/) makes developing applications for MacOS and iOS fast and fun.

[Swift 5.3 Basics](https://docs.swift.org/swift-book/LanguageGuide/TheBasics.html)

[Start Developing iOS Apps with Swift](https://developer.apple.com/library/archive/referencelibrary/GettingStarted/DevelopiOSAppsSwift/)

[Apple Developer Documentation](https://developer.apple.com/documentation)

[Apple Foundation Framework](https://developer.apple.com/documentation/foundation)

[Apple Core Animation Framework](https://developer.apple.com/documentation/quartzcore)

[Apple Core Graphics Framework](https://developer.apple.com/documentation/coregraphics)

[Virtualization Framework](https://developer.apple.com/documentation/virtualization)

[Paravirtualized Graphics Framework](https://developer.apple.com/documentation/paravirtualizedgraphics)

[Getting Started with LLDB](https://developer.apple.com/library/archive/documentation/IDEs/Conceptual/gdb_to_lldb_transition_guide/document/lldb-basics.html)

[Mac Catalyst - iOS - Human Interface Guidelines](https://developer.apple.com/design/human-interface-guidelines/ios/overview/mac-catalyst/)

[Amazon EC2 Mac Instances](https://aws.amazon.com/ec2/instance-types/mac/)

[Swift GitHub](https://github.com/apple/swift)

[Apple Developer Forums](https://developer.apple.com/forums/)

[Swift Forums](https://forums.swift.org/)

[Google's Swift Style Guide](https://google.github.io/swift/)

[Swift Courses Online from Coursera](https://www.coursera.org/courses?query=swift)

[Swift Courses Online from Udemy](https://www.udemy.com/topic/swift/)

[Learning Swift course from Codecademy](https://www.codecademy.com/learn/learn-swift)

## Swift Tools, Libraries, and Frameworks

[SceneKit](https://developer.apple.com/scenekit/) is a high-level 3D graphics framework that helps you create 3D animated scenes and effects in your iOS apps.

[Mac Catalyst](https://developer.apple.com/mac-catalyst/) is a set of Apple APIs that developers can use to rapidly port their iOS apps to [Apple Silicon M1 Chip](https://www.apple.com/mac/m1/) and take full advantage of the new capabilities on the new Apple hardware.

[Vapor](https://github.com/vapor/vapor) is a web framework for Swift. It provides a beautifully expressive and easy to use foundation for your next website, API, or cloud project.

[Hero](https://github.com/HeroTransitions/Hero) is a library for building iOS view controller transitions. It provides a declarative layer on top of the UIKit's cumbersome transition APIs—making custom transitions an easy task for developers.

[Kingfisher](https://github.com/onevcat/Kingfisher) is a powerful, pure-Swift library for downloading and caching images from the web. It provides you a chance to use a pure-Swift way to work with remote images in your next app.

[Realm](https://github.com/realm/realm-cocoa) is a mobile database that runs directly inside phones, tablets or wearables. This repository holds the source code for the iOS, macOS, tvOS & watchOS versions of Realm Swift & Realm Objective-C.

[Perfect](https://github.com/PerfectlySoft/Perfect) is a complete and powerful toolbox, framework, and application server for Linux, iOS, and macOS (OS X). It provides everything a Swift engineer needs for developing lightweight, maintainable, and scalable apps and other REST services entirely in the Swift programming language for both client-facing and server-side applications.

[Alamofire](https://github.com/Alamofire/Alamofire) is an HTTP networking library written in Swift.

[Eureka](https://github.com/xmartlabs/Eureka) is an elegant iOS form builder in Swift

[Carthage](https://github.com/Carthage/Carthage) is intended to be the simplest way to add frameworks to your Cocoa application. Carthage builds your dependencies and provides you with binary frameworks, but you retain full control over your project structure and setup. Carthage does not automatically modify your project files or your build settings.

[ReactiveCocoa](https://github.com/ReactiveCocoa/ReactiveCocoa) is reactive extensions to Cocoa frameworks, built on top of ReactiveSwift.

# Objective-C Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## Objective-C Learning Resources

[Objective-C](https://developer.apple.com/library/archive/documentation/Cocoa/Conceptual/ProgrammingWithObjectiveC/Introduction/Introduction.html) was the primary programming language used for writing software for macOS and iOS until [Swift](https://developer.apple.com/swift/) was introduced in 2014. It is a superset of the C programming language and provides object-oriented capabilities and a dynamic runtime.

[Apple Developer Forums](https://developer.apple.com/forums/)

[Google's Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)

[Objective C Courses on Coursera](https://www.coursera.org/courses?query=objective%20c)

[Objective-C online course on Udemy](https://www.udemy.com/topic/objective-c/)

[Objective-C for Swift Developers course by David Nutter](https://www.pluralsight.com/courses/objective-c-swift-developers)

[Objective-C Essential Training on LinkedIn Learning](https://www.linkedin.com/learning/objective-c-essential-training/)

[Objective-C for Swift Developers on Udacity](https://www.udacity.com/course/objective-c-for-swift-developers--ud1009)

## Objective-C Tools, Libraries, and Frameworks

[Cocoapods](https://cocoapods.org/) is a dependency manager for Swift and Objective-C in your Xcode projects by specifying the dependencies for your project in a simple text file. CocoaPods then recursively resolves dependencies between libraries, fetches source code for all dependencies, and creates and maintains an Xcode workspace to build your project.

[Realm](https://github.com/realm/realm-cocoa) is a mobile database(replaces Core Data & SQLite) that runs directly inside phones, tablets or wearables.

[Infer](https://github.com/facebook/infer) is a static analysis tool for Java, C++, Objective-C, and C.

[Mantle](https://github.com/Mantle/Mantle) is a model framework that makes it easy to write a simple model layer for your Cocoa or Cocoa Touch application.

[Quick](https://github.com/Quick/Quick) is a behavior-driven development framework for Swift and Objective-C.

[Aspects](https://github.com/steipete/Aspects) is a simple library for aspect oriented programming in Objective-C and Swift.

[Hammerspoon](https://github.com/Hammerspoon/hammerspoon) is a tool for powerful automation for macOS that acts as a bridge between the operating system and a Lua scripting engine.

[Nimbus](https://github.com/jverkoey/nimbus) is an iOS framework whose feature set grows only as fast as its documentation.

# C/C++ Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## C/C++ Learning Resources

[C++](https://www.cplusplus.com/doc/tutorial/) is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.

[C](https://www.iso.org/standard/74528.html) is a general-purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.

[Embedded C](https://en.wikipedia.org/wiki/Embedded_C) is a set of language extensions for the C programming language by the [C Standards Committee](https://isocpp.org/std/the-committee) to address issues that exist between C extensions for different [embedded systems](https://en.wikipedia.org/wiki/Embedded_system). The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.

[C & C++ Developer Tools from JetBrains](https://www.jetbrains.com/cpp/)

[Open source C++ libraries on cppreference.com](https://en.cppreference.com/w/cpp/links/libs)

[C++ Graphics libraries](https://cpp.libhunt.com/libs/graphics)

[C++ Libraries in MATLAB](https://www.mathworks.com/help/matlab/call-cpp-library-functions.html)

[C++ Tools and Libraries Articles](https://www.cplusplus.com/articles/tools/)

[Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html)

[Introduction C++ Education course on Google Developers](https://developers.google.com/edu/c++/)

[C++ style guide for Fuchsia](https://fuchsia.dev/fuchsia-src/development/languages/c-cpp/cpp-style)

[C and C++ Coding Style Guide by OpenTitan](https://docs.opentitan.org/doc/rm/c_cpp_coding_style/)

[Chromium C++ Style Guide](https://chromium.googlesource.com/chromium/src/+/master/styleguide/c++/c++.md)

[C++ Core Guidelines](https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md)

[C++ Style Guide for ROS](http://wiki.ros.org/CppStyleGuide)

[Learn C++](https://www.learncpp.com/)

[Learn C : An Interactive C Tutorial](https://www.learn-c.org/)

[C++ Institute](https://cppinstitute.org/free-c-and-c-courses)

[C++ Online Training Courses on LinkedIn Learning](https://www.linkedin.com/learning/topics/c-plus-plus)

[C++ Tutorials on W3Schools](https://www.w3schools.com/cpp/default.asp)

[Learn C Programming Online Courses on edX](https://www.edx.org/learn/c-programming)

[Learn C++ with Online Courses on edX](https://www.edx.org/learn/c-plus-plus)

[Learn C++ on Codecademy](https://www.codecademy.com/learn/learn-c-plus-plus)

[Coding for Everyone: C and C++ course on Coursera](https://www.coursera.org/specializations/coding-for-everyone)

[C++ For C Programmers on Coursera](https://www.coursera.org/learn/c-plus-plus-a)

[Top C Courses on Coursera](https://www.coursera.org/courses?query=c%20programming)

[C++ Online Courses on Udemy](https://www.udemy.com/topic/c-plus-plus/)

[Top C Courses on Udemy](https://www.udemy.com/topic/c-programming/)

[Basics of Embedded C Programming for Beginners on Udemy](https://www.udemy.com/course/embedded-c-programming-for-embedded-systems/)

[C++ For Programmers Course on Udacity](https://www.udacity.com/course/c-for-programmers--ud210)

[C++ Fundamentals Course on Pluralsight](https://www.pluralsight.com/courses/learn-program-cplusplus)

[Introduction to C++ on MIT Free Online Course Materials](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-introduction-to-c-january-iap-2011/)

[Introduction to C++ for Programmers | Harvard ](https://online-learning.harvard.edu/course/introduction-c-programmers)

[Online C Courses | Harvard University](https://online-learning.harvard.edu/subject/c)

## C/C++ Tools and Frameworks

[Visual Studio](https://visualstudio.microsoft.com/) is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.

[Visual Studio Code](https://code.visualstudio.com/) is a code editor redefined and optimized for building and debugging modern web and cloud applications.

[Vcpkg](https://github.com/microsoft/vcpkg) is a C++ Library Manager for Windows, Linux, and MacOS.

[ReSharper C++](https://www.jetbrains.com/resharper-cpp/features/) is a Visual Studio Extension for C++ developers developed by JetBrains.

[CLion](https://www.jetbrains.com/clion/features/) is a cross-platform IDE for C and C++ developers developed by JetBrains.

[Code::Blocks](https://www.codeblocks.org/) is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.

[CppSharp](https://github.com/mono/CppSharp) is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.

[Conan](https://conan.io/) is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems.

[High Performance Computing (HPC) SDK](https://developer.nvidia.com/hpc) is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.

[Thrust](https://github.com/NVIDIA/thrust) is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.

[Boost](https://www.boost.org/) is an educational opportunity focused on cutting-edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.

[Automake](https://www.gnu.org/software/automake/) is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.

[Cmake](https://cmake.org/) is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.

[GDB](http://www.gnu.org/software/gdb/) is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed.

[GCC](https://gcc.gnu.org/) is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.

[GSL](https://www.gnu.org/software/gsl/) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.

[OpenGL Extension Wrangler Library (GLEW)](https://www.opengl.org/sdk/libs/GLEW/) is a cross-platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.

[Libtool](https://www.gnu.org/software/libtool/) is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.

[Maven](https://maven.apache.org/) is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.

[TAU (Tuning And Analysis Utilities)](http://www.cs.uoregon.edu/research/tau/home.php) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.

[Clang](https://clang.llvm.org/) is a production quality C, Objective-C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.

[OpenCV](https://opencv.org/) is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Libcu++](https://nvidia.github.io/libcudacxx) is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.

[ANTLR (ANother Tool for Language Recognition)](https://www.antlr.org/) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.

[Oat++](https://oatpp.io/) is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.

[JavaCPP](https://github.com/bytedeco/javacpp) is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language.

[Cython](https://cython.org/) is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.

[Spdlog](https://github.com/gabime/spdlog) is a very fast, header-only/compiled, C++ logging library.

[Infer](https://fbinfer.com/) is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in [OCaml](https://ocaml.org/).

# Python Development
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

## Python Learning Resources

[Python](https://www.python.org) is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.

[Python Developer’s Guide](https://devguide.python.org) is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.

[Azure Functions Python developer guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference-python) is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the [Azure Functions developers guide](https://docs.microsoft.com/en-us/azure/azure-functions/functions-reference).

[CheckiO](https://checkio.org/) is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.

[Python Institute](https://pythoninstitute.org)

[PCEP – Certified Entry-Level Python Programmer certification](https://pythoninstitute.org/pcep-certification-entry-level/)

[PCAP – Certified Associate in Python Programming certification](https://pythoninstitute.org/pcap-certification-associate/)

[PCPP – Certified Professional in Python Programming 1 certification](https://pythoninstitute.org/pcpp-certification-professional/)

[PCPP – Certified Professional in Python Programming 2](https://pythoninstitute.org/pcpp-certification-professional/)

[MTA: Introduction to Programming Using Python Certification](https://docs.microsoft.com/en-us/learn/certifications/mta-introduction-to-programming-using-python)

[Getting Started with Python in Visual Studio Code](https://code.visualstudio.com/docs/python/python-tutorial)

[Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html)

[Google's Python Education Class](https://developers.google.com/edu/python/)

[Real Python](https://realpython.com)

[The Python Open Source Computer Science Degree by Forrest Knight](https://github.com/ForrestKnight/open-source-cs-python)

[Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)

[Intro to Python by W3schools](https://www.w3schools.com/python/python_intro.asp)

[Codecademy's Python 3 course](https://www.codecademy.com/learn/learn-python-3)

[Learn Python with Online Courses and Classes from edX](https://www.edx.org/learn/python)

[Python Courses Online from Coursera](https://www.coursera.org/courses?query=python)

## Python Frameworks, Libraries, and Tools

[Python Package Index (PyPI)](https://pypi.org/) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.

[PyCharm](https://www.jetbrains.com/pycharm/) is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.

[Python Tools for Visual Studio(PTVS)](https://microsoft.github.io/PTVS/) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.

[Django](https://www.djangoproject.com/) is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

[Flask](https://flask.palletsprojects.com/) is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.

[Web2py](http://web2py.com/) is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.

[AWS Chalice](https://github.com/aws/chalice) is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.

[Tornado](https://www.tornadoweb.org/) is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.

[HTTPie](https://github.com/httpie/httpie) is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.

[Scrapy](https://scrapy.org/) is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.

[Sentry](https://sentry.io/) is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.

[Pipenv](https://github.com/pypa/pipenv) is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.

[Python Fire](https://github.com/google/python-fire) is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.

[Bottle](https://github.com/bottlepy/bottle) is a fast, simple and lightweight [WSGI](https://www.wsgi.org/) micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the [Python Standard Library](https://docs.python.org/library/).

[CherryPy](https://cherrypy.org) is a minimalist Python object-oriented HTTP web framework.

[Sanic](https://github.com/huge-success/sanic) is a Python 3.6+ web server and web framework that's written to go fast.

[Pyramid](https://trypyramid.com) is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.

[TurboGears](https://turbogears.org) is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.

[Falcon](https://falconframework.org/) is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.

[Neural Network Intelligence(NNI)](https://github.com/microsoft/nni) is an open source AutoML toolkit for automate machine learning lifecycle, including [Feature Engineering](https://github.com/microsoft/nni/blob/master/docs/en_US/FeatureEngineering/Overview.md), [Neural Architecture Search](https://github.com/microsoft/nni/blob/master/docs/en_US/NAS/Overview.md), [Model Compression](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/Overview.md) and [Hyperparameter Tuning](https://github.com/microsoft/nni/blob/master/docs/en_US/Tuner/BuiltinTuner.md).

[Dash](https://plotly.com/dash) is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.

[Luigi](https://github.com/spotify/luigi) is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.

[Locust](https://github.com/locustio/locust) is an easy to use, scriptable and scalable performance testing tool.

[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython.

[NumPy](https://www.numpy.org/) is the fundamental package needed for scientific computing with Python.

[Pillow](https://python-pillow.org/) is a friendly PIL(Python Imaging Library) fork.

[IPython](https://ipython.org/) is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.

[GraphLab Create](https://turi.com/) is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.

[Pandas](https://pandas.pydata.org/) is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.

[PuLP](https://coin-or.github.io/pulp/) is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.

[Matplotlib](https://matplotlib.org/) is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.

## Contribute

- [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/CoreML-Guide/pulls).

## License
[Back to the Top](https://github.com/mikeroyal/CoreML-Guide#table-of-contents)

Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/).