machine-learning.md

July 15, 2021 · View on GitHub

Bookmarks tagged [machine-learning]

www.codever.land/bookmarks/t/machine-learning

Qdrant - Vector Search Engine

https://qdrant.tech/

Qdrant - Open-Source Vector Search Engine. The neural search engine developed in Rust :crab:. It allows embeddings or neural network encoders to be turned into full-fledged applications for matching, ...

Best Machine Learning Books (Updated for 2020)

https://blog.floydhub.com/best-machine-learning-books/

The list of the best machine learning & deep learning books for 2020.

CCV

https://github.com/liuliu/ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library. [BSD]

Fido

https://github.com/FidoProject/Fido

A highly-modular C++ machine learning library for embedded electronics and robotics. [MIT] website

flashlight

https://github.com/facebookresearch/flashlight

A fast, flexible machine learning library from Facebook AI Research written entirely in C++ and based on the ArrayFire tensor library. [BSD-3-Clause] website

MeTA

https://github.com/meta-toolkit/meta

A modern C++ data sciences toolkit. [MIT] website

Minerva

https://github.com/dmlc/minerva

A fast and flexible system for deep learning. [Apache2]

mlpack

https://github.com/mlpack/mlpack

A scalable c++ machine learning library. [LGPLv3] website

OpenCV

https://github.com/Itseez/opencv

Open Source Computer Vision Library. [BSD] website

Recommender

https://github.com/GHamrouni/Recommender

C library for product recommendations/suggestions using collaborative filtering (CF). [BSD]

RNNLIB

https://github.com/szcom/rnnlib

RNNLIB is a recurrent neural network library for sequence learning problems. [GPLv3]

SHOGUN

https://github.com/shogun-toolbox/shogun

The Shogun Machine Learning Toolbox. [GPLv3]

sofia-ml

https://code.google.com/p/sofia-ml/

The suite of fast incremental algorithms for machine learning. [Apache2]

VLFeat

https://github.com/vlfeat/vlfeat

The VLFeat open source library implements popular computer vision algorithms specialising in image understanding and local featurexs extraction and matching. [BSD-2-Clause] [website](http://www.vlfeat...

About Rust’s Machine Learning Community

https://medium.com/@autumn_eng/about-rust-s-machine-learning-community-4cda5ec8a790#.hvkp56j3f

The conversations on the introduction of the latest Rust Machine Learning crate, which was also the birthplace of the new rust-machine-learning IRC (thanks for the setup, @Argorak) lead to the…

AtheMathmo/rusty-machine

https://github.com/AtheMathmo/rusty-machine

Machine learning library for Rust Build Status

avinashshenoy97/RusticSOM

https://github.com/avinashshenoy97/RusticSOM

Rust library for Self Organising Maps (SOM). Build Status

autumnai/leaf

https://github.com/autumnai/leaf

Open Machine Intelligence framework. Build Status. Abandoned project. The most updated fork is [spe...

tensorflow/rust

https://github.com/tensorflow/rust

Rust language bindings for TensorFlow. Build Status

maciejkula/rustlearn

https://github.com/maciejkula/rustlearn

Machine learning crate for Rust. Circle CI

LaurentMazare/tch-rs

https://github.com/LaurentMazare/tch-rs

Rust language bindings for PyTorch. Build Status

FfDL

https://github.com/IBM/FfDL

Deep Learning Platform offering TensorFlow, Caffe, PyTorch etc. as a Service on Kubernetes

kubeflow

https://github.com/google/kubeflow

Machine Learning Toolkit for Kubernetes.

MLT

https://github.com/IntelAI/mlt

Machine Learning Container Templates: easy to use container and kubernetes object templates.

mxnet-operator

https://github.com/deepinsight/mxnet-operator

Tools for ML/MXNet on Kubernetes.

Polyaxon

https://github.com/polyaxon/polyaxon

An open source platform for reproducible machine learning and deep learning on kubernetes

seldon-core

https://github.com/SeldonIO/seldon-core

Open source framework for deploying machine learning models on Kubernetes

TensorFlow k8s

https://github.com/tensorflow/k8s

Tools for ML/Tensorflow on Kubernetes. Contribute to kubeflow/tf-operator development by creating an account on GitHub.

bayesian

https://github.com/jbrukh/bayesian

Naive Bayesian Classification for Golang.

CloudForest

https://github.com/ryanbressler/CloudForest

Fast, flexible, multi-threaded ensembles of decision trees for machine learning in pure Go.

eaopt

https://github.com/MaxHalford/eaopt

An evolutionary optimization library.

evoli

https://github.com/khezen/evoli

Genetic Algorithm and Particle Swarm Optimization library.

fonet

https://github.com/Fontinalis/fonet

A Deep Neural Network library written in Go.

go-cluster

https://github.com/e-XpertSolutions/go-cluster

Go implementation of the k-modes and k-prototypes clustering algorithms.

go-deep

https://github.com/patrikeh/go-deep

A feature-rich neural network library in Go.

go-fann

https://github.com/white-pony/go-fann

Go bindings for Fast Artificial Neural Networks(FANN) library.

go-galib

https://github.com/thoj/go-galib

Genetic Algorithms library written in Go / golang.

go-pr

https://github.com/daviddengcn/go-pr

Pattern recognition package in Go lang.

gobrain

https://github.com/goml/gobrain

Neural Networks written in go.

godist

https://github.com/e-dard/godist

Various probability distributions, and associated methods.

goga

https://github.com/tomcraven/goga

Genetic algorithm library for Go.

GoLearn

https://github.com/sjwhitworth/golearn

General Machine Learning library for Go.

golinear

https://github.com/danieldk/golinear

liblinear bindings for Go.

GoMind

https://github.com/surenderthakran/gomind

A simplistic Neural Network Library in Go.

goml

https://github.com/cdipaolo/goml

On-line Machine Learning in Go.

goRecommend

https://github.com/timkaye11/goRecommend

Recommendation Algorithms library written in Go.

gorgonia

https://github.com/chewxy/gorgonia

graph-based computational library like Theano for Go that provides primitives for building various machine learning and neural network algorithms.

gorse

https://github.com/zhenghaoz/gorse

A High Performance Recommender System Package based on Collaborative Filtering for Go.

goscore

https://github.com/asafschers/goscore

Go Scoring API for PMML.

gosseract

https://github.com/otiai10/gosseract

Go package for OCR (Optical Character Recognition), by using Tesseract C++ library.

libsvm

https://github.com/datastream/libsvm

libsvm golang version derived work based on LIBSVM 3.14.

mlgo

https://github.com/NullHypothesis/mlgo

This project aims to provide minimalistic machine learning algorithms in Go.

neat

https://github.com/jinyeom/neat

Plug-and-play, parallel Go framework for NeuroEvolution of Augmenting Topologies (NEAT).

neural-go

https://github.com/schuyler/neural-go

Multilayer perceptron network implemented in Go, with training via backpropagation.

ocrserver

https://github.com/otiai10/ocrserver

A simple OCR API server, seriously easy to be deployed by Docker and Heroku.

onnx-go

https://github.com/owulveryck/onnx-go

Go Interface to Open Neural Network Exchange (ONNX).

probab

https://github.com/ThePaw/probab

Probability distribution functions. Bayesian inference. Written in pure Go.

regommend

https://github.com/muesli/regommend

Recommendation & collaborative filtering engine.

shield

https://github.com/eaigner/shield

Bayesian text classifier with flexible tokenizers and storage backends for Go.

tfgo

https://github.com/galeone/tfgo

Easy to use Tensorflow bindings: simplifies the usage of the official Tensorflow Go bindings. Define computational graphs in Go, load and execute models trained in Python.

Varis

https://github.com/Xamber/Varis

Golang Neural Network.

AI4R

https://github.com/sergiofierens/ai4r

Algorithms covering several Artificial intelligence fields.

Awesome Machine Learning with Ruby

https://github.com/arbox/machine-learning-with-ruby

A Curated List of Ruby Machine Learning Links and Resources.

PredictionIO Ruby SDK

https://github.com/PredictionIO/PredictionIO-Ruby-SDK

The PredictionIO Ruby SDK provides a convenient API to quickly record your users' behavior and retrieve personalized predictions for them.

rb-libsvm

https://github.com/febeling/rb-libsvm

Ruby language bindings for LIBSVM. SVM is a machine learning and classification algorithm.

ruby-fann

https://github.com/tangledpath/ruby-fann

Ruby library for interfacing with FANN (Fast Artificial Neural Network).

rumale

https://github.com/yoshoku/rumale

A machine learninig library with interfaces similar to Scikit-Learn.

weka

https://github.com/paulgoetze/weka-jruby

Machine learning and data mining algorithms for JRuby.

H2O

https://github.com/h2oai/h2o-3

Open Source Fast Scalable Machine Learning Platform.

Metrics

https://github.com/benhamner/Metrics

Machine learning evaluation metrics.

NuPIC

https://github.com/numenta/nupic

Numenta Platform for Intelligent Computing.

scikit-learn

http://scikit-learn.org/

The most popular Python library for Machine Learning.

Spark ML

http://spark.apache.org/docs/latest/ml-guide.html

Apache Spark's scalable Machine Learning library.

vowpal_porpoise

https://github.com/josephreisinger/vowpal_porpoise

A lightweight Python wrapper for Vowpal Wabbit.

xgboost

https://github.com/dmlc/xgboost

A scalable, portable, and distributed gradient boosting library.

ConvNetJS

https://github.com/karpathy/convnetjs

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

DN2A

https://github.com/dn2a/dn2a-javascript

Digital Neural Networks Architecture.

Brain.js

https://github.com/harthur/brain

Neural networks in JavaScript.

Mind.js

https://github.com/stevenmiller888/mind

A flexible neural network library.

Synaptic.js

https://github.com/cazala/synaptic

Architecture-free neural network library for node.js and the browser.

TensorFlow.js

https://js.tensorflow.org

A JavaScript library for training and deploying ML models in the browser and on Node.js.

ml5.js

https://ml5js.org

Friendly Machine Learning for the Web.

https://flink.apache.org

Fast, reliable, large-scale data processing engine.

Apache Mahout

https://mahout.apache.org

Scalable algorithms focused on collaborative filtering, clustering and classification.

Apache Spark

https://spark.apache.org

Data analytics cluster-computing framework.

DatumBox

http://www.datumbox.com

Provides several algorithms and pre-trained models for natural language processing.

DeepDive

http://deepdive.stanford.edu

Creates structured information from unstructured data and integrates it into an existing database.

Deeplearning4j

https://deeplearning4j.org

Distributed and multi-threaded deep learning library.

H2O

https://www.h2o.ai

Analytics engine for statistics over big data.

JSAT

https://github.com/EdwardRaff/JSAT

Algorithms for pre-processing, classification, regression, and clustering with support for multi-threaded execution.

Oryx 2

https://github.com/OryxProject/oryx

Framework for building real-time, large-scale machine learning applications. Includes end-to-end applications for collaborative filtering, classification, regression, and clustering.

Smile

https://haifengl.github.io/smile

The Statistical Machine Intelligence and Learning Engine provides a set of machine learning algorithms and a visualization library.

Weka

https://www.cs.waikato.ac.nz/ml/weka

Collection of algorithms for data mining tasks ranging from pre-processing to visualization.

awesome-machine-learning

https://github.com/josephmisiti/awesome-machine-learning#readme

A curated list of awesome Machine Learning frameworks, libraries and software. - josephmisiti/awesome-machine-learning

awesome-Machine-Learning-Tutorials

https://github.com/ujjwalkarn/Machine-Learning-Tutorials#readme

machine learning and deep learning tutorials, articles and other resources - ujjwalkarn/Machine-Learning-Tutorials

awesome-machine-learning-with-ruby

https://github.com/arbox/machine-learning-with-ruby#readme

Curated list: Resources for machine learning in Ruby - arbox/machine-learning-with-ruby

awesome-Awesome-CoreML-Models

https://github.com/likedan/Awesome-CoreML-Models#readme

Largest list of models for Core ML (for iOS 11+). Contribute to likedan/Awesome-CoreML-Models development by creating an account on GitHub.

awesome-h2o

https://github.com/h2oai/awesome-h2o#readme

A curated list of research, applications and projects built using H2O Machine Learning - h2oai/awesome-h2o

awesome-dive-into-machine-learning

https://github.com/hangtwenty/dive-into-machine-learning#readme

Dive into Machine Learning with Python Jupyter notebook and scikit-learn! - hangtwenty/dive-into-machine-learning

Machine Learning From Scratch: The Perceptron Model - YouTube

https://www.youtube.com/watch?v=iumlHzoVlJM

Learn how to build a perceptron model from scratch with Javascript!

機械学習 はじめよう

http://gihyo.jp/dev/serial/01/machine-learning

中谷秀洋，恩田伊織

Mahoutで体感する機械学習の実践

http://gihyo.jp/dev/serial/01/mahout

やまかつ

Jubatus : オンライン機械学習向け分散処理フレームワーク

http://jubat.us/ja/index.html#table-of-contents

The Python Game Book

http://thepythongamebook.com/en%3Astart

The LION Way: Machine Learning plus Intelligent Optimization

http://www.e-booksdirectory.com/details.php?ebook=9575

The Elements of Statistical Learning

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Reinforcement Learning: An Introduction

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Probabilistic Models in the Study of Language

http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html

(Draft, with R code)

Neural Networks and Deep Learning

http://neuralnetworksanddeeplearning.com

Machine Learning, Neural and Statistical Classification

http://www1.maths.leeds.ac.uk/~charles/statlog/

Machine Learning

http://www.intechopen.com/books/machine_learning

Learning Deep Architectures for AI

http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf

(PDF)

Learn Tensorflow

https://bitbucket.org/hrojas/learn-tensorflow

Jupyter Notebooks

Introduction to Machine Learning

http://arxiv.org/abs/0904.3664v1

Amnon Shashua

Information Theory, Inference, and Learning Algorithms

http://www.inference.phy.cam.ac.uk/itila/

Gaussian Processes for Machine Learning

http://www.gaussianprocess.org/gpml/

Deep Learning

http://www.deeplearningbook.org

Ian Goodfellow, Yoshua Bengio and Aaron Courville

Bayesian Reasoning and Machine Learning

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage

An Introduction to Statistical Learning

http://www-bcf.usc.edu/~gareth/ISL/

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

A First Encounter with Machine Learning

https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf

(PDF)

A Course in Machine Learning

http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf

(PDF)

A Brief Introduction to Neural Networks

http://www.dkriesel.com/en/science/neural_networks