You can use that time to dive deeper into some aspects.NLP is used in automatic translation and spell checking, processing and analysing corpus document, simulation conversation (chat box), virtual assistants, automatic text generation (statistics used for making text understantable for all), automatic text generation, document classification, text summarisation, matching text with a set of criteria. We cannot assume you took this class so there will be ~3 lectures that overlap in content. The first problem set will probably be easier for you. Knowledge of convolutional neural networks (CS231n).You may find some of the optimization tricks more intuitive with this background. We will discuss a lot of different tasks and you will appreciate the power of deep learning techniques even more if you know how much work had been done on these tasks and how related models have solved them. Knowledge of natural language processing (CS224N or CS224U).We will be formulating cost functions, taking derivatives and performing optimization with gradient descent. Equivalent knowledge of CS229 (Machine Learning).You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. Basic Probability and Statistics (e.g.You should be comfortable taking derivatives and understanding matrix vector operations and notation. College Calculus, Linear Algebra (e.g.C/C++/Matlab/Javascript) you will probably be fine. If you have a lot of programming experience but in a different language (e.g. There is a tutorial here for those who aren't as familiar with Python. 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.

Deep learning in nlp