THE VIDEO SHELF · natural language processing
Lecture 1 | Natural Language Processing with Deep Learning
The first lecture of Stanford's Natural Language Processing with Deep Learning course, taught by Chris Manning and Richard Socher. It introduces NLP and word vectors, including SVD, skip-gram, CBOW, negative sampling and word2vec.
Why HN readers recommended it
Recommended as one of the best free courses for learning modern deep learning for NLP.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Stanford University School of Engineering
- Topic
- natural language processing
- Format
- lecture
- Level
- intermediate
- HN recommenders in dataset
- 3
- HN mentions in dataset
- 3
- Recommendation years in dataset
- 2017–2018
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any …
neel8986 on Hacker News · 2018-01
In Ask HN: How to Seriously Start with Machine Learning and AIFor those who like Prof Manning's style and want to get more in depth, here are the lecture videos for the NLP course he teaches at Stanford: [VIDEO]
ahussain on Hacker News · 2017-12
In Theoretical Machine Learning Lecture Series: Deep Learning and CognitionI have read like 1/3 of the comments but its interesting that the most interesting problem of everything we are talking about was only noticed by a non-programmer (so far). Everything that was talked about in the comments all hinge on the state of the …
morugin on Hacker News · 2018-03
In Amazon Is Hiring More Developers for Alexa Than Google Is Hiring for Everything
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.