Natural Language Processing offers a variety of techniques to get insight from and generate text data. Going beyond simple representations and taking advantage of Deep Learning and RNNs, the models can use document context to perform more accurately. With the help of libraries like TensorFlow, building neural networks and applying NLP is now available to the wider audience.
In this tutorial, Barbara will make the introduction to NLP concepts and deep learning architectures. The audience will be walked through two labs: sentiment analysis and text generation. After this session, the audience will have a good understanding of the deep learning concepts when it comes to NLP. The attendees will create a classifying model that takes advantage of the document context using TensorFlow library and scale their solutions using Google Cloud
Barbara is a Machine Learning Engineer with strong software development background. While working with a variety of different companies, she gained experience in building diverse software systems. This experience brought her focus to the Data Science and Big Data field. She believes in the importance of the data and metrics when growing a successful business. Alongside collaborating around data architectures, Barbara still enjoys programming activities. Currently speaking at conferences in-between working in London.