Schedule: Text, language, speech sessions

Transformer architectures have taken the field of natural language processing (NLP) by storm and pushed recurrent neural networks to the sidelines. Aurélien Géron examines transformers and the amazing language models based on them (e.g., BERT and GPT 2) and shows how you can use them in your projects. Read more.

Join KC Tung to discover a way to use TensorFlow to solve a natural language processing (NLP) model bias problem with data augmentation for an enterprise customer (one of the largest airlines in the world). KC leveraged hidden gems in tf.data and the new API to easily find a novel use for text generation and found it surprisingly improved his NLP model. Read more.

When people discuss on Twitter, the company wants to ensure that they can have respectful conversations with genuine people. Twitter relies on machine learning to improve the health of public conversations and information integrity. Li Xu and Yi Zhuang examine how Twitter uses TensorFlow to detect abusive, toxic, and spammy content and promotes healthy conversations on the platform. Read more.

OpenSeq2Seq provides a large set of state-of-the-art models and building blocks for automatic speech recognition (Jasper, wav2letter, DeepSpeech2), speech synthesis (Centaur, Tacotron2), and natural language processing. Jason Li and Vitaly Lavrukhin explore large vocabulary speech recognition and speech command recognition tasks to solve these problems with OpenSeq2Seq. Read more.