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

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnnPython codes for common Machine Learning Algorithms

linear-regression polynomial-regression logistic-regression decision-trees random-forest svm svr knn-classification naive-bayes-classifier kmeans-clustering hierarchical-clustering pca lda xgboost-algorithmcilantro is a lean and fast C++ library for working with point cloud data, with emphasis given to the 3D case. It includes efficient implementations for a variety of common operations, providing a clean API and attempting to minimize the amount of boilerplate code. The library is extensively templated, enabling operations on data of arbitrary numerical type and dimensionality (where applicable) and featuring a modular/extensible design of the more complex procedures. At the same time, convenience aliases/wrappers for the most common cases are provided. A high-level description of cilantro can be found in our technical report. Documentation (readthedocs.io, Doxygen API reference) is a work in progress. The short provided examples (built by default) cover a significant part of the library's functionality. Most of them expect a single command-line argument (path to a point cloud file in PLY format). One such input is bundled in examples/test_clouds for quick testing.

clustering point-cloud registration pca segmentation convex-hull k-means reconstruction mds ransac rgbd 3d 3d-visualization icp spectral-clustering convex mean-shift model-fitting iterative-closest-point non-rigid-registrationPrince uses pandas to manipulate dataframes, as such it expects an initial dataframe to work with. In the following example, a Principal Component Analysis (PCA) is applied to the iris dataset. Under the hood Prince decomposes the dataframe into two eigenvector matrices and one eigenvalue array thanks to a Singular Value Decomposition (SVD). The eigenvectors can then be used to project the initial dataset onto lower dimensions.The first plot displays the rows in the initial dataset projected on to the two first right eigenvectors (the obtained projections are called principal coordinates). The ellipses are 90% confidence intervals.

pandas pca ca mca svd factor-analysisImplicitly-restarted Lanczos methods for fast truncated singular value decomposition of sparse and dense matrices (also referred to as partial SVD). IRLBA stands for Augmented, Implicitly Restarted Lanczos Bidiagonalization Algorithm. The package provides the following functions (see help on each for details and examples).Help documentation for each function includes extensive documentation and examples. Also see the package vignette, vignette("irlba", package="irlba").

svd pca principal-component-analysis singular-value-decompositionPrincipal component analysis in Ruby. Uses GSL for calculations. PCA can be used to map data to a lower dimensional space while minimizing information loss. It's useful for data visualization, where you're limited to 2-D and 3-D plots.

pca principal-component-analysis rubymlImplement face recognition with pure Java

face-recognition pca eigenfaces fisherfaces lda lppOur first idea was to answer to this question: can we assess the quality of OpenStreetMap data? (and how?). This project is dedicated to explore and analyze the OpenStreetMap data history in order to classify the contributors.

openstreetmap luigi data-quality pca kmeans osm data-analysis machine-learning statisticsThis is one of many single cell courses/tutorials. An excellent list of all single cell package, courses, tutorials, speakers for conferences, can be found here. We'll use some additional dependencies outside of the scientific python ecosystem.

singlecell single-cell bioinformatics machinelearning pca tsne scikit-learn matplotlib seaborn jupyter-notebookH2O4GPU is a collection of GPU solvers by H2Oai with APIs in Python and R. The Python API builds upon the easy-to-use scikit-learn API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn (i.e. import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option. The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling. Daal library added for CPU, currently supported only x86_64 architecture.

gpu glm cuda c-plus-plus cpu lasso elastic-net svd pca rstats r machine-learningThis package provides various tools for classification, e.g., image classification, face recogntion, and related applicaitons. Run run_me_first for path configurations.

face-recognition classification classification-algorithims covariance-matrix sparse-coding linear-regression linear-discriminant-analysis principal-component-analysis symmetric-positive-definite spd subspace manifold matlab-toolbox dictionary-learning manifold-optimization support-vector-machines src eigenfaces pcaThis is a place for miscellaneous R and other code I've put together for clients, co-workers or myself for learning and demonstration purposes. The attempt is made to put together some well-commented and/or conceptually clear code from scratch, though most functionality is readily available in any number of well-developed R packages. Typically, examples are provided using such packages for comparison of results. I would say most of these are geared toward intermediate to advanced folks that want to dig a little deeper into the models and underlying algorithms. More recently, if it gets more involved, I usually just create a document of some kind rather than a standard *.R file, so you might check out the docs repo as well.

r stan jags matlab julia bayesian mixed-models gaussian-processes factor-analysis pca em survival-analysis ordinal-regression probit irt mixture-model zip lasso-regression additive-modelsProject dense vector representations of texts on a 2D plan to better understand neural models applied to NLP. Since the famous word2vec, embeddings are everywhere in NLP (and other close areas like IR). The main idea behind embeddings is to represent texts (made of characters, words, sentences, or even larger blocks) as numeric vectors. This works very well and provides some abilities unreachable with the classic BoW approach. However, embeddings (e.g. vector representations) are difficult to understand, analyze (and debug) for humans because they are made of much more than just 3 dimensions.

word-embeddings pca tsne plot graph shiny rstatsbrief: motionLib is a small lib in computer animation/graphic project, i used it in data compression and motion synthesis. note: these files may depend on each other,in most cases, you need to include them all in your project. And the CASEParser class is absent for license issue.

quaternion bezier interpolation pca c-plus-plus cubic-bezierThis is the R version assignments of the online machine learning course (MOOC) on Coursera website by Prof. Andrew Ng. This repository provides the starter code to solve the assignment in R statistical software; the completed assignments are also available beside each exercise file.

machine-learning learning-curve pca linear-regression gradient-descent svm principal-component-analysis clustering neural-network k-means recommender-system classification regularization anomalydetection ghIn 2018, The European Space Agency (ESA) organized a series of 6 lectures on Machine Learning at the European Space Operations Centre (ESOC). This repository contains the lectures resources: presentations, notebooks and links to the videos (presentation and hands-on).

machinelearning machine-learning linear-regression support-vector-machines decision-trees random-forest neural-network deep-learning clustering pca anomaly-detection text-mining tf-idf topic-modeling lectures lecture-slides lecture-material lecture-videosPrincipal component analysis

pca principal component analysis dimensionality reduction data mining datamining machine learning
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