Here are the videos and presentations of a course is organised on Deep NLP by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.
From the preamble:
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.
Phil Blunsom (Oxford University and DeepMind)
Chris Dyer (Carnegie Mellon University and DeepMind)
Edward Grefenstette (DeepMind)
Karl Moritz Hermann (DeepMind)
Andrew Senior (DeepMind)
Wang Ling (DeepMind)
Jeremy Appleyard (NVIDIA)
Zafarullah Mahmood just made available on YouTube the lectures that were available as mp4 on Github. The playlist is here and the orginal lecture notes are here on Github.
Deep Learning for NLP at Oxford 2017 - Leacure 1a - Introduction [Phil Blunsom] 19:07
Deep Learning for NLP at Oxford 2017 - Lecture 1b - Deep Neural Networks [Wang Ling]
Deep Learning for NLP at Oxford 2017 - Lecture 2a - Word Level Semantics [Ed Grefenstette]
Deep Learning for NLP at Oxford 2017 - Lecture 2b - Overview of the Practicals [Chris Dyer] 28:39
Deep Learning for NLP at Oxford 2017 - Lecture 3 - Language Modelling and RNNs Part 1 [Phil Blunsom] 1:11:16
Deep Learning for NLP at Oxford 2017 - Lecture 4 - Language Modelling and RNNs Part 2 [Phil Blunsom] 1:05:56
Deep Learning for NLP at Oxford 2017 - Lecture 5 - Text Classification [Karl Moritz Hermann]
Deep Learning for NLP at Oxford 2017 - Lecture 6 - Deep NLP on Nvidia GPUs [Jeremy Appleyard]
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.