Multiple implementations for abstractive text summurization , using google colab

Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Network

Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Oxford Deep NLP 2017 course - Practical 1: word2vec

訓練中文詞向量 Word2vec, Word2vec was created by a team of researchers led by Tomas Mikolov at Google.

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

借助Python抓取微博数据，并对抓取的数据进行情绪分析

Inspired by word2vec-pride-vis the replacement of words of Russian most valuable novels text with closest word2vec model words. By Boris Orekhov

All about Neural Networks!

Toolkit to obtain and preprocess german corpora, train models using word2vec (gensim) and evaluate them with generated testsets

Taking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead.

Remove problematic gender bias from word embeddings.

AraVec is a pre-trained distributed word representation (word embedding) open source project which aims to provide the Arabic NLP research community with free to use and powerful word embedding models.

A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.

Simple but useful Python web scraping tutorial code.

Implementation of Hierarchical Attention Networks in PyTorch

Log Anomaly Detection - Machine learning to detect abnormal events logs

Service Classification based on Service Description

A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs. Models: word2vec, CNNs, transformer, gpt.