文件名称：Oxford NLP lecture
文件大小：46.88MB
文件格式：ZIP
更新时间：2020-12-07 09:10:17
NLP
This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. Recently statistical techniques based on neural networks have achieved a number of remarkable successes in natural language processing leading to a great deal of commercial and academic interest in the field This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. These topics are organised into three high level themes forming a progression from understanding the use of neural networks for sequential language modelling, to understanding their use as conditional language models for transduction tasks, and finally to approaches employing these techniques in combination with other mechanisms for advanced applications. Throughout the course the practical implementation of such models on CPU and GPU hardware is also discussed. This course is organised by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.
【文件预览】：
lectures-master
----Lecture 5 - Text Classification.pdf(1.97MB)
----Lecture 4 - Language Modelling and RNNs Part 2.pdf(1.14MB)
----Lecture 12- Memory Lecture.pdf(3.65MB)
----Lecture 2b - Overview of the Practicals.pdf(1.23MB)
----Lecture 7 - Conditional Language Modeling.pdf(3.48MB)
----Lecture 8 - Conditional Language Modeling with Attention.pdf(7.51MB)
----Lecture 2a- Word Level Semantics.pdf(1.51MB)
----README.md(19KB)
----Lecture 6 - Nvidia RNNs and GPUs.pdf(1.26MB)
----Lecture 11 - Question Answering.pdf(5.94MB)
----Lecture 10 - Text to Speech.pdf(15.11MB)
----Lecture 1b - Deep Neural Networks Are Our Friends.pdf(3.4MB)
----Lecture 1a - Introduction.pdf(1.39MB)
----Lecture 9 - Speech Recognition.pdf(2.51MB)
----Lecture 13 - Linguistics.pdf(5.28MB)
----Lecture 3 - Language Modelling and RNNs Part 1.pdf(926KB)