Embeddings are numerical representations of data that capture semantic meaning for AI and machine learning systems. They convert data such as text, images, or audio into numerical vectors, allowing AI systems to measure similarity, understand context, and power applications such as semantic search, recommendation systems, clustering, and Retrieval-Augmented Generation (RAG).
An implementation of word2vec applied to [stanford philosophy encyclopedia](http://plato.stanford.edu/)
Visually informed embedding of word (VIEW) is a tool for transferring multimodal background knowledge to NLP algorithms.
Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Adaptive Skip-gram implementation in Julia
How positive is an IMDB movie review ?
Paragraph vector analysis of state of the union addresses
Network Embedding Survey and Resources
Web based semantic visualization tool
All you need for text preprocessing for NLP
Tensorflow implementation of "Tracking the World State with Recurrent Entity Networks" [https://arxiv.org/abs/1612.03969] by Henaff, Weston, Szlam, Bordes, and LeCun.
Generates binary word embeddings by analyzing Wikipedia
意味表現学習
Experiments with different ML techniques using TensorFlow and Sci-kit-learn in Python
generating scripts for 'The Simpsons' using an LSTM network.
MHC-antigen affinity prediction using TensorFlow.
Create MIDI note vector embeddings using GloVe (Global Vectors for Word Representation)
🚣 A simple recommendation engine (by way of convolutions and embeddings) written in TensorFlow
RDF type classification and clustering
Embeddings and Word2Vec