AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
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Updated
Jul 14, 2020 - Python
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Python code for common Machine Learning Algorithms
A lean C++ library for working with point cloud data
H2Oai GPU Edition
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Implement face recognition using PCA, LDA and LPP
PCA that iteratively replaces missing data
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms.
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
Explore high-dimensional datasets and how your algo handles specific regions.
Fast truncated singular value decompositions
Randomized Dimension Reduction Library
Effective Dimensionality Reduction for Word Embeddings.
Unsupervised / Semisupervised Anomaly Detection
A repository for recording the machine learning code
Randomized Matrix Decompositions using R
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.
R package: parallel computing toolset for relatedness and principal component analysis of SNP data (Development Version)
A MATLAB toolbox for classifier: Version 1.0.7
This is a Project Page of 'Towards a complete 3D morphable model of the human head'
a repository for my curriculum project
An approach to document exploration using Machine Learning. Let's cluster similar research articles together to make it easier for health professionals to find relevant research articles, and respond to rapidly spreading COVID-19 promptly.
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