Through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems.Īll class assignments will be in Python (and use numpy). On the model side we will cover word vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some very novel models involving a memory component. The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models.
These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. There are a large variety of underlying tasks and machine learning models powering NLP applications. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. Understanding complex language utterances is also a crucial part of artificial intelligence.
Natural language processing (NLP) is one of the most important technologies of the information age.
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