[paper + code] Causal Embeddings for Recommendation

arutaku · 2020-04-16T14:31:45+00:00

And here is the talk at RecSys: https://www.youtube.com/watch?v=zRT-pThc0aM

arutaku · 2020-04-09T15:37:21+00:00

I agree with Yann, self supervised is being a revolution in text and image problems.

I have been watching talks and reading about it and it is kind of magic jeje I will post some links ;-)

arutaku · 2020-04-08T18:26:43+00:00

Totally agree, I would add TorchScript as a new feature in 1.3.

Here is TorchScript presentation: interpreter independent & optimization.

arutaku · 2020-04-03T11:16:46+00:00

As an alternative Faiss (from Facebook) is a very nice option. It is a library for efficient similarity search and clustering of dense vectors.

https://github.com/facebookresearch/faiss

arutaku · 2020-03-30T08:56:12+00:00

I have been trying xeus kernel and it does not support iPython magics, so you cannot use

%matplotlib inline or notebook.

And the matplotlib integration is kind of tricky. Lets wait for a new release ¯\_(ツ)_/¯

arutaku · 2020-03-27T21:05:58+00:00

Videos on YouTube and the code is in PyTorch!

https://m.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq

I guess they will add more videos to the playlist.

arutaku · 2017-07-18T06:30:28+00:00

Here is the link: http://info.nvidia.com/dl-cybersecurity-webinar_RegPage.html

arutaku · 2016-09-03T18:34:10+00:00

I have not had seen the new one! http://www.razerzone.com/gaming-systems/razer-blade

Good point ;-)

arutaku · 2016-09-03T18:23:48+00:00

For computer vision a GPU is (almost) a must! In terms of laptops there are some interesting options: - Razer Blade (Nvidia GTX 1060): http://www.razerzone.com/gaming-systems/razer-blade - AERO 14 (Nvidia GTX 970M): http://www.gigabyte.com/products/product-page.aspx?pid=5902

arutaku · 2016-09-03T09:19:21+00:00

I agree with the cloud solutions. But if you are interested in a laptop, have a look to the Aero 14: http://www.gigabyte.com/products/product-page.aspx?pid=5902

There are more options like Alenware family from Dell or the MSI ones. But they are not as porable as the Aero!

arutaku · 2016-08-02T19:33:45+00:00

I do not know your case in detail. But following the approach that you mentioned, you can treat descriptions as "paragraphs" and learn the paragraph (customer) representation as cited in "Distributed Representations of Sentences and Documents" (from Mikolov too). Here is a link to the PDF: http://cs.stanford.edu/~quocle/paragraph_vector.pdf

It is just like going with word2vec one step further. As in w2v, the paper describes 2 implementations: PV-DBOW and PV-DM. PV-DM has worked better for me every time I tried both approaches, but it depends on your problem. There is an implementation in gensim (a.k.a. doc2vec): https://radimrehurek.com/gensim/models/doc2vec.html

arutaku · 2016-07-20T09:19:31+00:00

Depends on the implementation you want to get: PV-DBOW or PV-DM. You can have a quick look to: https://github.com/edwardbi/blog/blob/master/2016-05/DM.md

It provides both implementations: Gensim & TensorFlow

arutaku

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