Familia - A Toolkit for Industrial Topic Modeling

•    C++

---

A Toolkit for Industrial Topic Modeling

topic-modeling topic-models lda sentence-lda twe nlp

Machine-Learning-with-Python - Python codes for common Machine Learning Algorithms

•    Jupyter

---

Python 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-algorithm

lda - LDA topic modeling for node.js

•    Javascript

---

Latent Dirichlet allocation (LDA) topic modeling in javascript for node.js. LDA is a machine learning algorithm that extracts topics and their related keywords from a collection of documents. In LDA, a document may contain several different topics, each with their own related terms. The algorithm uses a probabilistic model for detecting the number of topics specified and extracting their related keywords. For example, a document may contain topics that could be classified as beach-related and weather-related. The beach topic may contain related words, such as sand, ocean, and water. Similarly, the weather topic may contain related words, such as sun, temperature, and clouds.

lda natural-language-processing nlp artificial-intelligence ai node nodejs node-js machine-learning topic-modeling topics language keywords latent-dirichlet-allocation latent-dirichlet dirichlet ml natural-language topic-model topic-modelling document words

trlda - Implementations of various online inference algorithms for LDA, with Python interface.

•    C++

---

Additional features include adaptive learning rates (Ranganath et al., 2013) and automatic tuning of hyperparameters via empirical Bayes. I have tested the code with the versions above, but older versions might also work.

machine-learning lda topic-modeling variational-inference

hlda - Gibbs sampler for the Hierarchical Latent Dirichlet Allocation topic model

•    Jupyter

---

Hierarchical Latent Dirichlet Allocation (hLDA) addresses the problem of learning topic hierarchies from data. The model relies on a non-parametric prior called the nested Chinese restaurant process, which allows for arbitrarily large branching factors and readily accommodates growing data collections. The hLDA model combines this prior with a likelihood that is based on a hierarchical variant of latent Dirichlet allocation.

gibbs-sampler hierarchical-topic-models lda topic-modeling topic-hierarchies

FaceRecognition - Implement face recognition using PCA, LDA and LPP

•    Java

---

Implement face recognition with pure Java

face-recognition pca eigenfaces fisherfaces lda lpp

lda-topic-modeling - A PureScript, browser-based implementation of LDA topic modeling.

•    PureScript

---

A PureScript, browser-based implementation of latent Dirichlet allocation (LDA) topic modeling. Able to take in two or more documents and soft cluster them by up to four topics. Try it at lettier.com/lda-topic-modeling. Read more about LDA.

lda topic-modeling data-science natural-language-processing nlp nlp-machine-learning purescript thermite machine-learning bayesian gibbs-sampling latent-dirichlet-allocation functional-programming clustering reactive-programming reactive machine-learning-algorithms bulma bulma-css text-mining

zAnalysis - 统计学工程应用，机器学习，模型呈现

•    Pascal

---

统计学工程应用，机器学习，模型呈现

learn pca lda sift hog feature analysis machine learn-pascal arm rasterization h264 yuv mpeg y4m bfgs

go-topics - Latent Dirichlet Allocation

•    Go

---

A very basic LDA (Latent Dirichlet Allocation) implementation with some convenient utilities.

lda topic model topic-model topics bayesian inference mcmc gibbs

PartiallyCollapsedLDA - Implementations of various fast parallelized samplers for LDA, including Partially Collapsed LDA, Light LDA, Partially Collapsed Light LDA and a very efficient Polya-Urn LDA

•    Java

---

Måns Magnusson, Leif Jonsson, Mattias Villani, and David Broman. (2017). Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models. Journal of Computational and Graphical Statistics. Alexander Terenin, Måns Magnusson, Leif Jonsson, and David Draper. “Polya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler”. Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017.

lda machine-learning machine-learning-algorithms machinelearning

Natural-Language-Processing-Fundamentals - Use Python and NLTK to build out your own text classifiers and solve common NLP problems

•    Jupyter

---

If NLP hasn't been your forte, Natural Language Processing Fundamentals will make sure you set off to a steady start. This comprehensive guide will show you how to effectively use Python libraries and NLP concepts to solve various problems. You'll be introduced to natural language processing and its applications through examples and exercises. This will be followed by an introduction to the initial stages of solving a problem, which includes problem definition, getting text data, and preparing it for modeling. With exposure to concepts like advanced natural language processing algorithms and visualization techniques, you'll learn how to create applications that can extract information from unstructured data and present it as impactful visuals. Although you will continue to learn NLP-based techniques, the focus will gradually shift to developing useful applications. In these sections, you'll understand how to apply NLP techniques to answer questions as can be used in chatbots. By the end of this course, you'll be able to accomplish a varied range of assignments ranging from identifying the most suitable type of NLP task for solving a problem to using a tool like spacy or gensim for performing sentiment analysis. The book will easily equip you with the knowledge you need to build applications that interpret human language.

nlp natural-language-processing tokenization supervised unsupervised linear-regression pandas scikit-learn binary-classifier latent-dirichlet-allocation lda api markov-chain