Hands-On Deep Learning with Go

Word-embeddings in Go

Word-embeddings in Go is an example of a task-specific ML library. It implements the two-layer neural network necessary to generate word embeddings, using Word2vec and GloVe. It is a great implementation, fast, and clean. It implements a limited number of features very well and in ways specific to the task of generating word embeddings via Word2vec and GloVe.

An example of this is a core feature required for training DNNs, an optimization method called SGD. This is used in the GloVe model, developed by a team at Stanford. However, the code is integrated specifically with the GloVe model, and the additional optimization methods used in Word2Vec (negative sampling and skip-gram) are not useful with DNNs.

This can be useful ...