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bidaf-elmo_bayesian

Implementation of the ELMo + BiDAF architecture where the point value weights have been replaced by Gaussian Random variables which are trained using a modification of the Bayes by BackProp algorithm

bootstraptrain

Bootstrap5

dana

demo-

this is a demo repository

document_similarity_algorithms_experiments

Document similarity algorithms experiment - Jaccard, TF-IDF, Doc2vec, USE, and BERT.

dona-frj

Config files for my GitHub profile.

dsf2020

Data Science Festival Workshop 7 November 2020 – Building a fashion recommender using Tensorflow/Keras with ASOS.

fashion-mnist-by-resnet

Train Fashion-MNIST by ResNet With TensorFlow 2.0 Alpha

flaskproject-semantic

jstutorial

meetups

mysemanticwebsite

Django CrachCourse

semantic

semantic-text-similarity-using-elmo-cosine-similarity

This repo shows how to generate text embedding using ELMO and compute Similarity using Cosine Similarity.

sentiment-analysis-using-dl-based-language-representation-learning-models

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. Deep Learning algorithms are unable to deal with textual data in their natural language data form which is typically unstructured information; they require special representation of data as inputs instead. Usually, natural language text data needs to be converted into internal representations form that DL algorithms can read such as feature vectors, hence the necessity to use representation learning models. These models have shown a big leap during the last years. Their set ranges from the methods that embed words into distributed representations and use the language modeling objective to adjust them as model parameters (like Word2vec, fastText, and GloVe), to recently transfer learning models (like ELMo, BERT, ULMFiT, XLNet, ALBERT, RoBERTa, and GPT-2). These last use larger corpora, more parameters, more computing resources, and instead of assigning each word with a fixed vector, they use multilayer neural networks to calculate dynamic representations for the words according to their context, which is especially useful for the words with multiple meanings.

simple-sentence-similarity

Exploring the simple sentence similarity measurements using word embeddings

starter-web

Simple starting point website project based upon Initializr

website