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CoreML-Guide

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

Last synced: 7 days ago
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C/C++ Learning Resources

C/C++ Tools and Frameworks

  • Visual Studio - 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
  • ReSharper C++
  • CLion - platform IDE for C and C++ developers developed by JetBrains.
  • Code::Blocks
  • Conan
  • High Performance Computing (HPC) SDK
  • Boost - 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
  • Cmake - 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
  • GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
  • GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
  • OpenGL Extension Wrangler Library (GLEW) - 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
  • Maven
  • TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
  • Clang - 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 - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
  • ANTLR (ANother Tool for Language Recognition)
  • Oat++ - efficient web application. It's zero-dependency and easy-portable.
  • Cython
  • Infer - C, and C. Infer is written in OCaml.
  • Vcpkg
  • CppSharp
  • Thrust - 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.
  • JavaCPP
  • Spdlog - only/compiled, C++ logging library.

Computer Vision Learning Resources

Computer Vision Tools, Libraries, and Frameworks

  • Automated Driving Toolbox™ - 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.
  • Image Processing Toolbox™ - 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™
  • Statistics and Machine Learning Toolbox™
  • Lidar Toolbox™ - camera cross calibration for workflows that combine computer vision and lidar processing.
  • Mapping Toolbox™
  • UAV Toolbox
  • Parallel Computing Toolbox™ - 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™
  • ROS Toolbox
  • Robotics Toolbox™ - holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
  • Deep Learning Toolbox™ - 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™ - making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
  • Deep Learning HDL Toolbox™ - 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™ - loop simulations, you can evaluate controller performance.
  • Vision HDL Toolbox™ - 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 - source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop 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.
  • LRSLibrary - 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™ - 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.
  • OpenCV - time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

Learning Resources for ML

Metal Learning Resources

Metal Tools, Libraries, and Frameworks

ML Frameworks, Libraries, and Tools

  • Amazon SageMaker
  • Azure Databricks - 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.
  • Apple CoreML - 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.
  • Apache MXNet
  • AutoGluon - accuracy deep learning models on tabular, image, and text data.
  • Jupyter Notebook - 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.
  • XGBoost
  • nGraph - of-use to AI developers.
  • Cluster Manager for Apache Kafka(CMAK)
  • Tensorman
  • cuML - learn.
  • Azure Databricks - 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.
  • AutoGluon - accuracy deep learning models on tabular, image, and text data.
  • TensorFlow - 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.

NLP Learning Resources

NLP Tools, Libraries, and Frameworks

  • PyTorch
  • Apache OpenNLP - 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, Sentence Detection, POS(Part-Of-Speech) tagging, Tokenization Feature extraction, Chunking, Parsing, and Coreference resolution.
  • Open Neural Network Exchange(ONNX) - in operators and standard data types.
  • Anaconda
  • Scikit-Learn
  • NVIDIA cuDNN - accelerated library of primitives for deep neural networks. 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, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
  • Apache Spark - 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 PredictionIO
  • BigDL
  • Eclipse Deeplearning4J (DL4J) - 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.
  • Chainer - 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 for high performance training and inference.
  • Natural Language Toolkit (NLTK) - to-use interfaces to over 50 corpora and lexical resources 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.
  • Tensorflow_macOS - optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
  • PlaidML
  • Caffe
  • Theano - dimensional arrays efficiently including tight integration with NumPy.
  • Apache Spark Connector for SQL Server and Azure SQL - 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.
  • Numba - 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.
  • CoreNLP
  • NLPnet - of-speech tagging, semantic role labeling and dependency parsing.
  • Flair - 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 - trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.

Objective-C Learning Resources

Objective-C Tools, Libraries, and Frameworks

  • AppKit
  • Instruments - 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 - 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.
  • AppCode - 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.
  • Infer - C, and C.
  • Mantle
  • Quick - driven development framework for Swift and Objective-C.
  • Nimbus
  • Aspects - C and Swift.
  • Hammerspoon
  • Xcode

Python Frameworks, Libraries, and Tools

  • Python Package Index (PyPI)
  • PyCharm
  • Django - level Python Web framework that encourages rapid development and clean, pragmatic design.
  • Web2py - 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.
  • Falcon - performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
  • Pillow
  • IPython