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GitHub - oxford-cs-deepnlp-2017/lectures: Oxford Deep NLP 2017 course

#artificialintelligence

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. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.


oxford-cs-deepnlp-2017/lectures

#artificialintelligence

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. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.


Oxford Course on Deep Learning for Natural Language Processing - Machine Learning Mastery

#artificialintelligence

If you are practitioner interested in deep learning for NLP, you may have different goals and requirements from the material. For example, you may want to focus on the methods and applications rather than the foundational theory. The course is comprised of 13 lectures, although the first and second lectures are both split into two parts. The complete lecture breakdown is provided below. The GitHub repository for the course provides links to slides, flash videos and reading for each lecture.


5 Free Resources for Getting Started with Deep Learning for Natural Language Processing

@machinelearnbot

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length.


Neural networks and deep learning with Microsoft Azure GPU

#artificialintelligence

The rise of neural networks and deep learning is correlated with increased computational power introduced by general purpose GPUs. The reason is that the optimisation problems being solved to train a complex statistical model, are demanding and the computational resources available are crucial to the final solution. Using a conventional CPU, one could spend weeks of waiting for a simple neural network to be trained. This problem is amplified when one is trying to spawn multiple experiments to select optimal parameters of a model. Having computational resources such as a high-end GPU is an important aspect when one begins to experiment with deep learning models as this allows a rapid gain in practical experience.