All Projects → machinelearnjs → Machinelearnjs

machinelearnjs / Machinelearnjs

Licence: mit
Machine Learning library for the web and Node.

Programming Languages

typescript
32286 projects

Projects that are alternatives of or similar to Machinelearnjs

Text Classification Benchmark
文本分类基准测试
Stars: ✭ 18 (-96.39%)
Mutual labels:  svm, random-forest
introduction-to-machine-learning
A document covering machine learning basics. 🤖📊
Stars: ✭ 17 (-96.59%)
Mutual labels:  random-forest, svm
Jsmlt
🏭 JavaScript Machine Learning Toolkit
Stars: ✭ 22 (-95.58%)
Mutual labels:  svm, random-forest
Pytorch classification
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
Stars: ✭ 395 (-20.68%)
Mutual labels:  svm, random-forest
SentimentAnalysis
(BOW, TF-IDF, Word2Vec, BERT) Word Embeddings + (SVM, Naive Bayes, Decision Tree, Random Forest) Base Classifiers + Pre-trained BERT on Tensorflow Hub + 1-D CNN and Bi-Directional LSTM on IMDB Movie Reviews Dataset
Stars: ✭ 40 (-91.97%)
Mutual labels:  random-forest, svm
Hyperparameter Optimization Of Machine Learning Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
Stars: ✭ 516 (+3.61%)
Mutual labels:  svm, random-forest
Machine-Learning-Models
In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.
Stars: ✭ 30 (-93.98%)
Mutual labels:  random-forest, svm
Ml Projects
ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python
Stars: ✭ 127 (-74.5%)
Mutual labels:  svm, random-forest
Bag-of-Visual-Words
🎒 Bag of Visual words (BoW) approach for object classification and detection in images together with SIFT feature extractor and SVM classifier.
Stars: ✭ 39 (-92.17%)
Mutual labels:  svm, feature-extraction
Bike-Sharing-Demand-Kaggle
Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand
Stars: ✭ 33 (-93.37%)
Mutual labels:  random-forest, feature-extraction
handson-ml
도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (-42.77%)
Mutual labels:  random-forest, svm
Breast-Cancer-Scikitlearn
simple tutorial on Machine Learning with Scikitlearn
Stars: ✭ 33 (-93.37%)
Mutual labels:  random-forest, svm
Amazon-Fine-Food-Review
Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review
Stars: ✭ 28 (-94.38%)
Mutual labels:  random-forest, svm
Machine Learning With Python
Python code for common Machine Learning Algorithms
Stars: ✭ 3,334 (+569.48%)
Mutual labels:  svm, random-forest
Python Ml Course
Curso de Introducción a Machine Learning con Python
Stars: ✭ 442 (-11.24%)
Mutual labels:  svm
Machine Learning
⚡机器学习实战(Python3):kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
Stars: ✭ 5,601 (+1024.7%)
Mutual labels:  svm
Blastula
Easily send great-looking HTML email messages from R
Stars: ✭ 394 (-20.88%)
Mutual labels:  easy-to-use
Vehicle Detection
Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
Stars: ✭ 462 (-7.23%)
Mutual labels:  svm
Awesome Feature Engineering
A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
Stars: ✭ 433 (-13.05%)
Mutual labels:  feature-extraction
User Machine Learning Tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Stars: ✭ 393 (-21.08%)
Mutual labels:  random-forest

machinelearn.js

machinelearn.js is a Machine Learning library written in Typescript. It solves Machine Learning problems and teaches users how Machine Learning algorithms work.

Build Status Build status FOSSA Status Slack ZenHub

User Installation

Using yarn

$ yarn add machinelearn

Using NPM

$ npm install --save machinelearn

On the browsers

We use jsdeliver to distribute browser version of machinelearn.js

<script src="https://cdn.jsdelivr.net/npm/machinelearn/machinelearn.min.js"></script>
<script>
    const { RandomForestClassifier } = ml.ensemble;
    const cls = new RandomForestClassifier();
</script>

Please see https://www.jsdelivr.com/package/npm/machinelearn for more details.

Accelerations

By default, machinelearning.js will use pure Javascript version of tfjs. To enable acceleration through C++ binding or GPU, you must import machinelearn-node for C++ or machinelearn-gpu for GPU.

  1. C++
  • installation
yarn add machinelearn-node
  • activation
import 'machinelearn-node';
  1. GPU
  • installation
yarn add machinelearn-gpu
  • activation
import 'machinelearn-gpu';

Highlights

  • Machine Learning on the browser and Node.js
  • Learning APIs for users
  • Low entry barrier

Development

We welcome new contributors of all level of experience. The development guide will be added to assist new contributors to easily join the project.

  • You want to participate in a Machine Learning project, which will boost your Machine Learning skills and knowledge
  • Looking to be part of a growing community
  • You want to learn Machine Learning
  • You like Typescript ❤️ Machine Learning

Simplicity

machinelearn.js provides a simple and consistent set of APIs to interact with the models and algorithms. For example, all models have follow APIs:

  • fit for training
  • predict for inferencing
  • toJSON for saving the model's state
  • fromJSON for loading the model from the checkpoint

Testing

Testing ensures you that you are currently using the most stable version of machinelearn.js

$ npm run test

Supporting

Simply give us a 🌟 by clicking on

Contributing

We simply follow "fork-and-pull" workflow of Github. Please read CONTRIBUTING.md for more detail.

Further notice

Great references that helped building this project!

Contributors

Thanks goes to these wonderful people (emoji key):


Jason Shin

📝 🐛 💻 📖 ⚠️

Jaivarsan

💬 🤔 📢

Oleg Stotsky

🐛 💻 📖 ⚠️

Ben

💬 🎨 📢 🐛 💻

Christoph Reinbothe

💻 🤔 🚇 👀

Adam King

💻 ⚠️ 📖
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].