text2vec - Fast vectorization, topic modeling, distances and GloVe word embeddings in R.

•    R

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text2vec is an R package which provides an efficient framework with a concise API for text analysis and natural language processing (NLP). To learn how to use this package, see text2vec.org and the package vignettes. See also the text2vec articles on my blog.

word2vec text-mining natural-language-processing glove vectorization topic-modeling word-embeddings latent-dirichlet-allocation

Repo-2017 - Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano

•    Python

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Welcome to my GitHub repo. I am a Data Scientist and I code in R, Python and Wolfram Mathematica. Here you will find some Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence models I developed.

anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocation

magnitude - A fast, efficient universal vector embedding utility package.

•    Python

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A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. Vector space embedding models have become increasingly common in machine learning and traditionally have been popular for natural language processing applications. A fast, lightweight tool to consume these large vector space embedding models efficiently is lacking.

natural-language-processing nlp machine-learning vectors embeddings word2vec fasttext glove gensim fast memory-efficient machine-learning-library word-embeddings

pycadl - Python package with source code from the course "Creative Applications of Deep Learning w/ TensorFlow"

•    Python

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This package is part of the Kadenze Academy program Creative Applications of Deep Learning w/ TensorFlow. from cadl import and then pressing tab to see the list of available modules.

deep-learning neural-network tutorial mooc gan vae vae-gan pixelcnn wavenet magenta nsynth tensorflow celeba cyclegan dcgan word2vec glove autoregressive conditional course

GloVe-as-a-TensorFlow-Embedding-Layer - Taking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**

•    Jupyter

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In this tutorial, we'll see how to convert GloVe embeddings to TensorFlow layers. This could also work with embeddings generated from word2vec. First, we'll download the embedding we need.

gpu gpu-acceleration gpu-tensorflow word-embeddings word2vec glove-embeddings tensorflow cosine-similarity glove neural-network tensorflow-layers

wego - Word2Vec, GloVe in Go!

•    Go

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This is the implementation of word embedding (a.k.a word representation) models in Golang. Like this example, the models generate the vectors that could calculate word meaning by arithmetic operations for other vectors.

word2vec glove machine-learning natural-language-processing shallow-neural-network gorgonia dep word-embeddings

GloVe-experiments - GloVe word vector embedding experiments (similar to Word2Vec)

•    Python

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This repository contains a few brief experiments with Stanford NLP's GloVe, an unsupervised learning algorithm for obtaining vector representations for words. Similar to Word2Vec, GloVe creates a continuous N-dimensional representation of a word that is learned from its surrounding context words in a training corpus. Trained on a large corpus of text, these co-occurance statistics (an N-dimensional vector embedding) cause semantically similar words to appear near each-other in their resulting N-dimensional embedding space (e.g. "dog" and "cat" may appear nearby a region of other pet related words in the embedding space because the context words that surround both "dog" and "cat" in the training corpus are similar). All three scripts use the GloVe.6B pre-trained word embeddings created from the combined Wikipedia 2014 and Gigaword 5 datasets. They were trained using 6 billion tokens and contains 400,000 unique lowercase words. Trained embeddings are provided in 50, 100, 200, and 300 dimensions (822 MB download).

glove-embeddings glove-vectors word2vec k-means glove nlp machine-learning embeddings word-game k-nearest-neighbors