While deep learning has rapidly emerged as the dominant approach to training predictive models for large-scale machine learning problems, these algorithms push the limits of available hardware, requiring specialized frameworks optimized for GPUs and distributed cloud-based training. Moreover, especially in natural language processing (NLP), models contain a variety of moving parts: character-based encoders, pre-trained word embeddings, long-short term memory (LSTM) cells, and beam search for decoding sequential outputs, among others.

This tutorial introduces GluonNLP, a powerful new toolkit that combines MXNet’s speed, the user-friendly Gluon frontend, and an extensive new library automating the most painful aspects of deep learning for NLP. In this full-day tutorial, we will start off with a crash course on deep learning with Gluon, covering data, autodiff, and deep (convolutional and recurrent) neural networks. Then we’ll dive into GluonNLP, demonstrating how to work with word embeddings (both pre-trained and from scratch), language models, and the popular Transformer model for machine translation.

Time and location will be posted when available.

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