Top C++ Based Data Science And Machine Learning Libraries

Dynamic load balancing, adaptive caching, and the creation of comprehensive big data frameworks and libraries are all best done in C++. The vast majority of the deep learning libraries listed below, including MongoDB and Google’s MapReduce, have been developed in C++. Scylla is a database management system developed in C++ and an alternative to Apache Cassandra and Amazon DynamoDB because of its incredibly low latency and high throughput.

C++ is the finest language to use when developing large big data frameworks and libraries, dynamic load balancing, and adaptive caching. MongoDB and Google’s MapReduce are examples of C++-developed deep-learning libraries included in the list below. Scylla is a database management system created in C++ and is an alternative to Apache Cassandra and Amazon DynamoDB due to its exceptionally low latency and excellent throughput.

A potential rival to Python in scientific computing and data processing is Julia, a compiled and interactive language created by MIT. Integrating and using C++ as a data science and extensive data library has been made easier for developers and data scientists by its quick processing speed, parallelism, static and dynamic typing, and C++ bindings for plugging in libraries.

Let’s take a deeper look at several C++ libraries that can be helpful for both conventional and deep learning models for every data scientist.

TensorFlow from Google AI

Google created the well-known Deep Learning Library, which has its own ecosystem of resources for researchers and developers to easily create and deploy ML-powered apps.

Caffe from Berkeley

The Berkeley Vision and Learning Center created Convolutional Architecture for Fast Feature Embedding, or Caffe, a deep learning system built in C++.

Microsoft Cognitive Toolkit (CNTK)

A unified deep-learning toolbox called Microsoft Cognitive Toolkit assists in translating neural networks into a sequence of computing operations via a directed graph.

mlpack Library

mlpack is a C++ machine learning package that offers cutting-edge machine learning algorithms with C++ classes, Python bindings, and Julia bindings. It is quick and adaptable.

DyNet

DyNet is a high-performance neural network library created in C++ (with bindings in Python) that operates effectively on CPU or GPU. It allows computational graphs on the fly. It supports reinforcement learning, graph architectures, natural language processing, and other techniques.

Shogun

Shogun is an open-source machine-learning library that provides a variety of unified and effective machine-learning techniques, including mixing several data representations, algorithm classes, and all-purpose tools for quickly prototyping data pipelines.

FANN

A Fast Artificial Neural Network is a multilayer artificial neural network written in C with support for sparsely connected and fully connected networks. Additionally, it supports training DL models via backpropagation and changing topology-based training. Both fixed and floating point cross-platform execution is supported.

OpenNN

Open Neural Networks (OpenNN) is an open-source, high-performance neural network toolkit for C/C++ that supports forecasting, classification, regression, and other advanced analytics.

SHARK Library

Shark is a universal open-source machine learning library (C/C++) that is quick, modular, and supports many machine learning methods, including neural networks, linear and nonlinear optimization, and kernel-based learning algorithms.

Armadillo

The Armadillo linear algebra (C/C++) package has Matlab-like features. The library is renowned for its ability to quickly translate research code for various fields, including pattern recognition, computer vision, signal processing, bioinformatics, statistics, and econometrics.

Faisis

For effective similarity search and grouping of dense vectors, this library (C/C++) is utilized. It has algorithms that can search through vector set collections of any size, including those that would not fit in RAM. Additionally, it has an optional Python interface and optional GPU capabilities given by CUDA.

Boosting

XGBoost is a general-purpose gradient boosting library that has been parallelized and optimized.

ThunderGBM is a short library for Random Forests and GBDTs on GPUs.

LightGBM is a fast, distributed, high-performance gradient-boosting framework developed by Microsoft for ranking, classification, and various other machine-learning problems. It is based on decision tree techniques.

CatBoost is a general-purpose gradient-boosting decision tree library with out-of-the-box support for categorical features. It supports CPU and GPU (even multi-GPU) processing, is simple to install, and has a quick inference implementation.

Recommendation Systems

Recommender is a C library that uses collaborative filtering to provide product recommendations and suggestions (CF).

A hybrid recommender system built on scikit-learn algorithms is called a hybrid recommender system.

Natural Language Processing

BLLIP Parser is a Natural Language Parser for BLLIP (also known as the Charniak-Johnson parser).

Colibri-core is a C++ library, command-line tool, and Python wrapper for quickly and efficiently extracting and working with fundamental language structures like n-grams and skiagrams.

CRF++ is an open-source implementation of conditional random fields (CRFs) for applications related to natural language processing and segmenting/labeling sequential data. [Deprecated]

CRFsuite is a Conditional Random Fields (CRFs) implementation that labels sequential data. [Deprecated]

MeTA – ModErn Text Analysis, a C++ Data Sciences Toolkit, makes it simpler to mine vast amounts of text data with the help of deep semantic features, including parse trees, topic models, classification algorithms, graph algorithms, language models, multithreaded algorithms, etc.

The MIT Information Extraction Toolkit contains named entity recognition and relation extraction tools in C, C++, and Python.

ucto is a regular-expression-based tokenizer for many languages that is aware of Unicode. a C++ library and a tool. Has FoLiA format support.

General-Purpose Machine Learning

Darknet is an open-source neural network framework that supports CPU and GPU computing. It was created in C and CUDA.

A pure C (99) runtime called cONNXr is designed for small embedded devices with no dependencies. Installs quickly and builds on all platforms, even on highly ancient devices. No matter what framework you used to train your machine learning models, run inference on them.

A straightforward Multi-Armed Bandit library is BanditLib. [Deprecated]

Convolutional deep learning is implemented quickly in CUDA using the C++ language.

DeepDetect is a C++11-based machine learning server and API. Modern machine learning is now simple to use and incorporate into current systems.

Enables the training of models across numerous machines using big data sets. Microsoft’s Networked Machine Learning Tool Kit (DMTK) is a distributed machine learning framework (parameter server). LightLDA and Distributed (Multisense) Word Embedding have currently included tools.

DLib is a collection of simple machine-learning tools to integrate into other programs.

DSSTNE is software developed by Amazon for using GPUs to train and deploy deep neural networks that prioritize speed and scale over experimental flexibility.

Networks having dynamic structures that alter with each training instance function well with the dynamic neural network library known as DyNet. Written in C++ and using Python bindings.

Fido is a C++ machine learning package with a high degree of modularity for embedded robotics and electronics.

igraph is a graph library with several uses.

A high-performance software library created by Intel and tailored for Intel’s architectures is known as Intel(R) DAAL. The library offers algorithmic building blocks for all phases of data analytics and enables distributed, online, and batch data processing.

libfm is a general strategy that enables feature engineering to replicate most factorization models.

A database created for machine learning is called MLDB, or The Machine Learning Database. To instruct it to save data, send it commands using a RESTful API. The data may then be explored using SQL.

MXNet is a lightweight, portable, and adaptable distributed/mobile deep learning platform for Python, R, Julia, Go, Javascript, and other programming languages. It also has a dynamic, mutation-aware dataflow dep scheduler.

ProNet-core, a Pair-wise representations optimization in a general-purpose network embedding framework, edits the network.

Python’s CUDA interface is called PyCUDA.

ROOT is a framework for modular scientific software. All the features required for big data processing, statistical analysis, visualization, and storage are offered.

Shark is an open-source C++ machine-learning package that is quick, modular, and feature-rich.

A group of quick incremental algorithms called SOFIA-ML.

Stan is a probabilistic programming language that uses Hamiltonian Monte Carlo sampling and full Bayesian statistical inference.

Timbl is a collection of software and a C++ library that implements several memory-based learning algorithms, including IB1-IG, a k-nearest neighbor classification implementation, and IGTree, a decision-tree approximation of IB1-IG. Frequently employed in NLP.

An efficient outside-the-core learning system is Vowpal Wabbit (VW).

Warp-CTC is a quick, CPU and GPU-compatible parallel implementation of Connectionist Temporal Classification (CTC).

A fast SVM library for CPUs and GPUs is ThunderSVM.

A C++11 header-only neural network library called LKYDeepNN. native traditional Chinese document with little reliance.

xLearn is a high-performance, user-friendly, and scalable machine-learning software program that may be used to address complex machine-learning issues. Large-scale sparse data problems, frequently encountered in Internet services like online advertising and recommender systems, make use of xLearn particularly well.

A library for automated feature engineering is called Featuretools. It excels at employing reusable feature engineering “primitives” to transform transactional and relational datasets into feature matrices for machine learning.

Skynet is a library for building neural networks that features a C interface and a JSON-based network set. written in C++ and has Python, C++, and C# bindings.

A Feast is a feature store that allows users to manage, find, and access machine learning features. Feast offers a consistent view of the feature data for model serving and training.

Hopsworks is a data-intensive AI platform with the first open-source feature store in the market. The Hopsworks Feature Store offers a feature warehouse for training and batch applications based on Apache Hive and a feature serving database for online applications based on MySQL Cluster.

A platform for deep learning and machine learning that is scalable and reproducible is called Polyaxon.

Sara is a C++ computer vision library that offers simple and effective computer vision algorithm implementations. Version 2 of the Mozilla Public License .0]

A GPU (CUDA) based Artificial Neural Network library is ANNetGPGPU. [LGPL]

Game Behavior Tree Starter Kit is known as btsk. [zlib]

A template-based, ANSI-C++ evolutionary computation package called Evolving Objects makes it incredibly quick to create your own stochastic optimization algorithms. [LGPL]

Frugally-deep is a header-only library for C++ that supports Keras models. [MIT]

Genann is a basic C library for neural networks. [zlib]

MXNet is a lightweight, portable, and adaptable distributed/mobile deep learning framework for Python, R, Julia, Scala, Go, JavaScript, and other programming languages. It also has a dynamic, mutation-aware dataflow dep scheduler.

Tensors and dynamic neural networks in Python using PyTorch, which has powerful GPU acceleration.

websiteRecast/Detour is a pathfinder and navigation mesh generator for games in three dimensions. [zlib]

A dependency-free, header-only deep learning framework written in C++11 is called tiny-dnn. [BSD]

Veles is a distributed platform for developing deep learning software quickly. [Apache]

Toolkit for voice recognition called Kaldi. [Apache]

Computer Vision

A modern computer vision library, CCV is a C-based/Cached/Core Computer Vision Library.

The open-source, portable VLFeat library of computer vision algorithms comes with a Matlab toolbox.

DLib includes interfaces in C++ and Python for training broad object detectors and face detection.

The object-oriented C++ library called EBLearn [Deprecated] implements several machine-learning models.

OpenCV is compatible with Windows, Linux, Android, Mac OS and offers interfaces in C++, C, Python, Java, and MATLAB.

VIGRA is a general-purpose, cross-platform C++ library for computer vision and machine learning for volumes with any number of dimensions.

A real-time library for multi-person keypoint detection for body, face, hands, and foot estimates is called Openpose. From Facebook Research, FlashLight

FlashLight from Facebook Research

Torch is a quick and adaptable machine learning library developed by the Facebook AI Research Speech team, who also made torch and deep speech.

Mobile Neural Network from Alibaba

An extraordinarily effective and compact deep learning framework is MNN. It supports the inference and training of deep learning models and offers the best on-device inference and training performance in the market.

Habitat-SIM from Facebook Research

Before applying newly acquired skills in the actual world, embodied AI agents (virtual robots) can be trained using the habitat-sim (C++) library in a highly lifelike & effective 3D simulator. AI uses static datasets (such as ImageNet, COCO, and VQA) to train agents to behave realistically in their surroundings.

GRT (Gesture Recognition Toolkit)

Gesture Recognition Toolkit, or GRT, is a free, multi-platform C++ library. It was created specifically to recognize gestures in real-time. It has a specific C++ API reinforced by a tidy and user-friendly GUI (Graphical User Interface).

GRT is not only user-friendly for beginners, but it is also straightforward to incorporate into already-existing C++ programs. You may train it with your individual motions, which is compatible with any sensor or data input. Additionally, GRT can adjust your feature extraction or processing methods as and when necessary.