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Andrej Karpathy
6,852 followers -
Computer Science PhD student at Stanford. I love technology, robots, and artificial intelligence
Computer Science PhD student at Stanford. I love technology, robots, and artificial intelligence

6,852 followers
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Andrej's posts

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New blog post on "Deep Reinforcement Learning: Pong from Pixels".

Policy Gradients are powerful in certain settings, but further progress in rapid model building is necessary to push further.

http://karpathy.github.io/2016/05/31/rl/

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Didn't post on G+ in a while, so I thought I'd advertise my recent side project obsession *arxiv-sanity* (http://www.arxiv-sanity.com/top) . This site helps you sort through arxiv papers (especially in Machine Learning): search, sort by similarity, add papers to a library.

It will build you a personalized SVM based on tfidf of all papers in your library, and recommend new ones from Arxiv (recent or not).

I <3 working on this project because arxiv was just out of control.

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New blog post: Shaking things up a bit with a short story on AI, "A Cognitive Discontinuity".
http://karpathy.github.io/2015/11/14/ai/

Appropriately, names of 2 main characters (Merus, Licia) were sampled from my earlier list of RNN-generated names :)
https://plus.google.com/+AndrejKarpathy/posts/GuutNpJKCUp

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New fun side project blog post on "What a Deep Neural Network thinks about your #selfie".

http://karpathy.github.io/2015/10/25/selfie/

#RandomExperimentSundays : I was curious if char-rnn (https://github.com/karpathy/char-rnn) can generate new, fun and plausible baby names. So I got a dataset of 8,000 baby names from an NLP repo (http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/nlp/corpora/names/), trained a 2-layer LSTM and generated some.

To my amusement many fun unique names come out and 90% of them are not found in the training data. Here are 100 example samples that do not occur in training data:

Rudi
Levette
Berice
Lussa
Hany
Mareanne
Chrestina
Carissy
Marylen
Hammine
Janye
Marlise
Jacacrie
Hendred
Romand
Charienna
Nenotto
Ette
Dorane
Wallen
Marly
Darine
Salina
Elvyn
Ersia
Maralena
Minoria
Ellia
Charmin
Antley
Nerille
Chelon
Walmor
Evena
Jeryly
Stachon
Charisa
Allisa
Anatha
Cathanie
Geetra
Alexie
Jerin
Cassen
Herbett
Cossie
Velen
Daurenge
Robester
Shermond
Terisa
Licia
Roselen
Ferine
Jayn
Lusine
Charyanne
Sales
Sanny
Resa
Wallon
Martine
Merus
Jelen
Candica
Wallin
Tel
Rachene
Tarine
Ozila
Ketia
Shanne
Arnande
Karella
Roselina
Alessia
Chasty
Deland
Berther
Geamar
Jackein
Mellisand
Sagdy
Nenc
Lessie
Rasemy
Guen
Gavi
Milea
Anneda
Margoris
Janin
Rodelin
Zeanna
Elyne
Janah
Ferzina
Susta
Pey
Castina

Here is a much bigger sample: http://cs.stanford.edu/people/karpathy/namesGenUnique.txt

Some of my favorites include "Baby" (haha), "Killie", "Char", "R", "More", "Mars", "Hi", "Saddie", "With" and "Ahbort". Well that was fun.

Odd experience with ICCV reviews. I was first asked to order ~30 papers based on how qualified I was to review them. I spent a good hour on this, I liked many of them (based on the abstract) and ended up marking ~15 as qualified for.

Except of my final 5 assigned papers, 4 of them I can't remember ever seeing, not only in my final 15 but also in the wider list of 30. I can't tell if this is a mistake, or if this is working as intended. In the end, I'm very not qualified for 3/5 of the assigned papers. As a result, it's taking significantly more time and effort to write the reviews because I need to do my own literature review, and despite my efforts the 3 papers are unlikely to get an informed opinion.

Curious about experience of others - is this normal?

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CVPR 2015 papers are now up so I organized them into my annual pretty interface http://cs.stanford.edu/people/karpathy/cvpr2015papers/ … This year: new interactive t-SNE map

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New (epic) blog post on "The Unreasonable Effectiveness of Recurrent Neural Networks" http://karpathy.github.io/2015/05/21/rnn-effectiveness/ was immense fun to write

(sorry to people who are seeing this multiple times)

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Fooling Linear Classifiers on ImageNet
http://karpathy.github.io/2015/03/30/breaking-convnets/
new blog post with a few interpretations of the fooling ConvNets papers, along with some experiments with fooling linear classifiers. I tried to structure parts of it in ways that laymen could understand because I've been seeing quite a few misconceptions surround the topic online.

(sorry to ppl seeing this twice)

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The Final Course Projects for our ConvNet class have been posted (100 ConvNet projects!):
http://cs231n.stanford.edu/reports.html

(sorry to people who are seeing this 2+ times)
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