250 Open source projects that are alternatives of or similar to ml-simulations

经典机器学习算法的极简实现

Simple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

machine learning algorithms in Swift

Migrated to: https://gitlab.com/Oslandia/osm-data-classification

Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

Python Clustering Algorithms

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

Implementations of machine learning algorithm by Python 3

OCR Machine Learning in python

Efficient similarity search and clustering for Ruby

Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

Explore high-dimensional datasets and how your algo handles specific regions.

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

AI100文本分类竞赛代码。从传统机器学习到深度学习方法的测试

Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Deep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE

⭐ A Python package for optimal 1D k-means clustering.

文本聚类（Kmeans、DBSCAN、LDA、Single-pass）

Performance of various open source GBM implementations

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

《统计学习方法》与常见机器学习模型(GBDT/XGBoost/lightGBM/FM/FFM)的原理讲解与python和类库实现

A fast xgboost feature selection algorithm

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.

An End to End Customer Churn Prediction solution using AWS services.

Different clustering approaches applied on different problemsets

Building Annoy Index on Apache Spark

Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889

⏱ Benchmarks of machine learning inference for Go

Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies

Advanced High Performance Data Science Toolbox for R by Laurae

No description or website provided.

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

This project is to convert PortraitFCN+ (by Xiaoyong Shen) from Matlab to Tensorflow, then refine the outputs from it (converted to a trimap) using KNN and ResNet, supervised by Richard Berwick.

Stacked Generalization (Ensemble Learning)

Project Page of Combining 3D Morphable Models: A Large scale Face-and-Head Model - [CVPR 2019]

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Playing with MNIST. Machine Learning. Generative Models.

Provide an input CSV and a target field to predict, generate a model + code to run it.

A 100%-Julia implementation of Gradient-Boosting Regression Tree algorithms

A collection of research papers on decision, classification and regression trees with implementations.

A repository to keep track of all the code that I end up writing for my blog posts.

Workshop on tidytranscriptomics: Performing tidy transcriptomics analyses with tidybulk, tidyverse and tidyheatmap

Show how to perform fast retraining with LightGBM in different business cases

Solution to the Outbrain Click Prediction competition

Comparison tools

Implemented an A/B Testing solution with the help of machine learning

State-of-the art Automated Machine Learning python library for Tabular Data

Nyoka is a Python library to export ML/DL models into PMML (PMML 4.4.1 Standard).

Material of the Kaggle Berlin meetup group!

Data and Code to reproduce results for my talk at Paris: R in Insurance 2017 Conference

Maximum Covariance Analysis in Python

[UNMAINTAINED] Automated machine learning for analytics & production

Common library for serving TensorFlow, XGBoost and scikit-learn models in production.

To identify the driver's drowsiness based on real-time camera image and image processing techniques. 졸음운전 감지 시스템. OpenCV