{"id":12,"title":"Deep Learning for Natural Language Processing","url":"https://doi.org/10.26180/13100513","description":"This workshop is designed to be instructor led and consists of two parts.\r\nPart 1 consists of a lecture-demo about text processing and a hands-on session for attendees to learn how to clean a dataset.\r\nPart 2 consists of a lecture introducing Recurrent Neural Networks and a hands-on session for attendees to train their own RNN.\r\nThe Powerpoints contain the lecture slides, while the Jupyter notebooks (.ipynb) contain the hands-on coding exercises.\r\n\r\nThis workshop introduces natural language as data for deep learning. We discuss various techniques and software packages (e.g. python strings, RegEx, NLTK, Word2Vec) that help us convert, clean, and formalise text data “in the wild” for use in a deep learning model. We then explore the training and testing of a Recurrent Neural Network on the data to complete a real world task. We will be using TensorFlow v2 for this purpose.","doi":"","licence":"CC-BY-4.0","contact":"datascienceplatform@monash.edu","keywords":["deep learning","NLP","machine learning"],"remote_updated_date":null,"remote_created_date":null,"created_at":"2021-08-31T05:33:16.614Z","updated_at":"2021-09-07T10:35:47.447Z","content_provider_id":15,"target_audience":[],"authors":["Titus Tang"],"contributors":[],"subsets":[],"resource_type":[],"other_types":"","version":"","status":"active","date_created":null,"date_modified":null,"date_published":null,"prerequisites":"","syllabus":"","learning_objectives":"","fields":[],"scientific_topics":[],"operations":[]}