Skip to content

Commit 7471bd4

Browse files
committed
Qcon talk
1 parent 032dcc7 commit 7471bd4

28 files changed

+1
-1
lines changed

_posts/2019-05-27-skipgram-recommender-talk.md

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,7 @@ I gave a talk at [Qcon London](https://qconlondon.com/) this year. Watch it here
1616

1717
In this video, I introduced word embeddings and the word2vec algorithm. I then proceeded to discuss how the word2vec algorithm is used to create recommendation engines in companies like Airbnb and Alibaba. I close by glancing at real-world consequences of popular recommendation systems like those of YouTube and Facebook.
1818

19-
My [Illustrated Word2vec](/illustrated-word2vec/) post used and built on the materials I created for this talk (but didn't include anything on the recommender application of word2vec). This was my first talk at a technical conference and I spent quite a bit of time preparing for it. In the six weeks prior to the conference I spent about 100 hours working on the presentation and ended up with 200 slides. It was an interesting balancing act of trying to make it introductory but not shallow, suitable for senior engineers and architects yet not necessarily ones who have machine learning experience. The Qcon organizers helped prepare us with seminars on giving technical talks. I found those useful.
19+
My [Illustrated Word2vec](/illustrated-word2vec/) post used and built on the materials I created for this talk (but didn't include anything on the recommender application of word2vec). This was my first talk at a technical conference and I spent quite a bit of time preparing for it. In the six weeks prior to the conference I spent about 100 hours working on the presentation and ended up with 200 slides. It was an interesting balancing act of trying to make it introductory but not shallow, suitable for senior engineers and architects yet not necessarily ones who have machine learning experience.
2020

2121

2222

37.8 KB
Loading
57.1 KB
Loading
33.2 KB
Loading
Loading
Loading
Loading
49.5 KB
Loading
Loading
Loading
61.5 KB
Loading
82.6 KB
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
Loading
54.8 KB
Loading
27.3 KB
Loading
45.1 KB
Loading

0 commit comments

Comments
 (0)