what is lectures fr?

oxford-cs-deepnlp-2017/lectures — explained in plain English

Analysis updated 2026-06-24

★ 15,855Audience · researcherComplexity · 1/5Setup · easy

what do people make with this?

VIBE 1

Work through the full Oxford Deep NLP 2017 curriculum to build a foundation in neural networks for language tasks.

VIBE 2

Use the practical coding exercises to get hands-on experience with word embeddings and recurrent neural networks.

VIBE 3

Watch video lectures from DeepMind and CMU researchers on machine translation, speech recognition, and question answering.

how it stacks up fr

	oxford-cs-deepnlp-2017/lectures	game1024/openspeedy	521xueweihan/git-tips
Stars	15,855	15,860	15,849
Language	—	C++	—
Setup difficulty	easy	easy	easy
Complexity	1/5	3/5	1/5
Audience	researcher	general	developer

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

in plain english

This repository contains the lecture slides and materials from Oxford University's Deep Natural Language Processing (NLP) course, delivered in early 2017 in partnership with researchers from DeepMind. NLP is the field of teaching computers to understand and generate human language, things like translating text, answering questions, or transcribing speech. The course is advanced and assumes prior knowledge of machine learning. It covers how neural networks (AI systems inspired loosely by the brain) can be applied to language tasks. Topics include word embeddings (turning words into numbers that capture meaning), recurrent neural networks (models that process sequences of words one at a time), and how these can be used for tasks like language translation, speech-to-text, and question answering. Each lecture has PDF slides and a video recording linked directly in the README. There are also practical coding exercises for hands-on experience with the concepts covered. The course was organized by Phil Blunsom and included lecturers from Carnegie Mellon University, DeepMind, and NVIDIA. The full README is longer than what was provided, but the repository is essentially an open-access archive of the course's educational materials.

prompts (copy fr)

prompt 1

I'm working through the Oxford Deep NLP 2017 course on recurrent neural networks. Help me implement a basic RNN language model in Python based on the concepts from that curriculum.

prompt 2

Based on the Oxford 2017 Deep NLP course, explain how word embeddings capture semantic meaning and help me write code to train simple word vectors on a small text corpus.

prompt 3

Help me replicate the sequence-to-sequence translation architecture described in the Oxford Deep NLP 2017 lectures, starting from the encoder-decoder concept in the slides.

prompt 4

Using the Oxford 2017 NLP course as a guide, help me build a simple question-answering system that takes a passage and a question as input and returns a span answer.

Frequently asked questions