Oxford x DeepMind NLP Course Video - Word Vectors and Semantic

A Big Data Digest production, reprint requirements see the end of the article.

Big Data Digest proudly presents the University of OxfordDeep Learning and Natural Language Processing Course (Deep NLP) dubbed video!

Lecture 2a-1 Word Vectors and Lexical Semantics (Part 1)

Content of this session: Taught by Ed Grefenstette from DeepMind, discussing word vectors and lexical semantics. How do we encode words? Why is ‘cat’ just a vector in your model and not a fluffy cute creature?

Watch now ▼

Click Read Original to watch the ad-free complete video with Chinese subtitles!

Introduction to the Oxford Deep NLP Course

The Oxford Deep NLP course is an advanced course on Natural Language Processing (NLP). It is co-offered by Oxford University and Google DeepMind (the developers of AlphaGo) and is the latest course from the Department of Computer Science at Oxford University for the Spring Semester of 2017. The course is taught by Phil Blunsom, with guest lectures from several industry experts from DeepMind and NVIDIA.

Big Data Digest has contacted the course instructor to obtain translation authorization and has collaborated with the Pattern Recognition Laboratory of Beijing University of Posts and Telecommunications to organize the video dubbing, which is released for free.

So everyone can see Chinese subtitles now! Exciting, isn’t it!

Course Requirements

To study this course, you need a basic knowledge of probability theory, linear algebra, and calculus, and a solid foundation in machine learning and programming; no background in linguistics is required.

You Will Gain

Upon completing this course, you will be able to:

Understand the definitions of different neural network models
Derive and implement optimization algorithms for these models
Understand the neural implementation of attention mechanisms and sequential embedding models, as well as how to combine these modules to build NLP systems
Have an understanding of the hardware issues involved in implementing scalable neural network models on language data
Implement and evaluate commonly used neural network models in natural language processing

How to Participate in the Course

Click Read Original to watch the ad-free complete video with Chinese subtitles (updated to lecture 2a-1)!
All course materials (videos, handouts, assignments) are available for free on GitHub.

Course English materials can be found at:

https://github.com/oxford-cs-deepnlp-2017/lectures

For those who want to bookmark it, you can open this link on your computer to watch

http://www.xiniuedu.com/livecourse/detailinfo/public/html/detail.html?course_id=1017

The dubbed videos are continuously updated, with a new episode released every week. Please learn together with the Digest!

Additionally, many participants have reported that they find it hard to understand the public course and do not know who to ask for help with assignments.

The public course team leader, Digest, has once again stepped in to form a study group for the course. The group will release our organized study materials and will also organize live streams for assignment explanations!

How to Watch Assignment Explanation Live

Scan the QR code below to join the study group.

↓↓↓ Scan to join the study group ↓↓↓

Oxford x DeepMind NLP Course Video - Word Vectors and Semantic

↑↑↑ Scan to join the study group ↑↑↑

Group Joining Tips:

Due to WeChat restrictions, only 100 people can scan to join the group. Digest has set up a temporary group. For those who want to join the Oxford study group, please first join this temporary group, and after joining, please actively add the group leader as a friend. The group leader will add you to the course study group. Alternatively, you can directly add the assistant (WeChat ID: AI_Learner) and note “Oxford” to invite you to join the group.

Those who have already joined the previous Oxford Deep NLP group do not need to rejoin this temporary group.

Public sharing guest invitations in progress…

We welcome all kinds of interesting content, from explaining assignments to industry cutting-edge applications. If you have something to share, please let the group leader know after joining the group. You might even get to rap 😀

Special thanks:

To the translation team from the Pattern Recognition Laboratory of Beijing University of Posts and Telecommunications

Translation:

Li Nan, Min Feng, Qiao Yi Ning, Zhang Shi Ping

Dao Ge, Momo, Invincible Qiao Kater

Final Review:

Fanli, Lu Yawen (Alvin LO)

Peng Junyi, Long Muxue

Post-production:

Guo Li (Terminator Subtitles)

Project Management:

Long Muxue, Li Nan