This repository provides lecture materials for the Oxford Deep NLP 2017 course, targeting advanced students and researchers interested in applying neural networks to natural language processing. It offers a structured curriculum covering sequential language modeling, transduction tasks, and advanced applications, with a focus on practical implementation and recent advancements.

How It Works

The course material progresses from foundational concepts like word embeddings and recurrent neural networks (RNNs) to more complex topics such as attention mechanisms, sequence-to-sequence models, and applications like speech recognition and question answering. It emphasizes the mathematical underpinnings of machine learning models and their optimization, alongside practical considerations for GPU implementation.

Quick Start & Requirements

• Materials: Lecture slides and videos are available for each topic.
• Prerequisites: Familiarity with basic machine learning concepts, Python, and deep learning fundamentals is recommended.
• Resources: Links to relevant research papers, blog posts, and datasets are provided for further study.

Highlighted Details

• Covers a broad range of NLP tasks including language modeling, text classification, machine translation, speech recognition, and question answering.
• Features lectures from prominent researchers from Oxford University and DeepMind.
• Includes practical assignments focusing on word2vec, text classification, and RNNs.
• Discusses GPU optimization for deep learning models.

Maintenance & Community

The course was delivered in Hilary Term 2017. Discussion is facilitated via a Piazza page.

Licensing & Compatibility

The repository content is for educational purposes. Specific licensing for individual materials is not explicitly stated but is generally associated with academic courseware.

Limitations & Caveats

This repository contains materials from a 2017 course, and while foundational concepts remain relevant, the field of Deep NLP has advanced significantly since then. Some specific techniques or models mentioned may be outdated or superseded by newer approaches.