270 Open source projects that are alternatives of or similar to Deeplearning

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

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

🌳 Stacked Gradient Boosting Machines

A full pipeline AutoML tool for tabular data

Stacked Generalization (Ensemble Learning)

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

[Done] Master version: developed the stacked regression (score 0.11, top 5%) based on (xgboost, sklearn). Branch v1.0: developed linear regression (score 0.45) based on Tensorflow

This repository is the PyTorch implementation of GAN Ensemble for Anomaly Detection.

cheat sheet for Artificial Intelligence

2017第一届腾讯社交广告高校算法大赛Rank28_code

Code for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021

An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.

Another A/B test library

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

Bayesian Optimization and Grid Search for xgboost/lightgbm

Full Bayesian Inference for Hidden Markov Models

AutoGluon: AutoML for Text, Image, and Tabular Data

speechpro.com/

Machine learning models for time series analysis

Bayesian Segmentation of Spatial Transcriptomics Data

Official implementation codes of "Regularizing neural networks for future trajectory prediction via IRL framework"

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

a solution for competition of kaggle `Human or Robot`

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

arxiv.org/abs/1801.07710v2

A data-driven tool to identify the best candidates for a marketing campaign and optimize it.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

A repository for some of the code I used in kaggle data science & machine learning tasks.

bayesplot R package for plotting Bayesian models

CmdStanR: the R interface to CmdStan

住房月租金预测大数据赛TOP1

Original implementation of Calibrated Boosting-Forest

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

Use advanced feature engineering strategies and select best features from your data set with a single line of code.

Tiny Gradient Boosting Tree

An implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".

Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.

Scaleable input gradient regularization

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks

An extension of XGBoost to probabilistic forecasting

机器学习-Coursera-吴恩达- python+Matlab代码实现

Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.

Bayesian neural networks in PyTorch

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

Kaggle getting started competition examples

Merlion: A Machine Learning Framework for Time Series Intelligence

💪 🤔 Modern Super Learning with Machine Learning Pipelines

Materials of the Nordic Probabilistic AI School 2021.

Secure collaborative training and inference for XGBoost.

Machine learning for C# .Net

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data

Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

The complete code and notebooks used for the ACM Recommender Systems Challenge 2019

Machine Learning models from scratch with a better visualisation

A fast approximation to a Dirichlet Process Mixture model (DPM) for clustering genetic data

Implementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder

Deep and Machine Learning for Microscopy

Functions for phylogenetic analyses

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04