PyText

- PyText https://github.com/facebookresearch/pytext from Facebook:
- TLDR - FastText meets PyTorch;
- Very similar to AllenNLP in nature;
- Will be useful if you can afford to write modules for their framework to solve 100 identical tasks (i.e. like Facebook with 200 languages);
- In itself - seems to be too high maintenance to use;

I will not use use it.

#nlp
#deep_learning

DS/ML digest 32

Highlights:
- A way to replace softmax in NMT;
- Large visual reasoning dataset;
- PyText;

https://spark-in.me/post/2018_ds_ml_digest_32

#digest
#deep_learning
#data_science

Spell-checking on various scales in Russian

Bayes + n-gram rules = spell-checker for words / sentences

https://habr.com/company/joom/blog/433554/

#nlp

Practical creepiness

Now Google Photos explicitly shows that it knows faces of your family members.

#deep_learning

(My GPUs are ~70C under full load xD)

Sentiment datasets in Russian

Just randomly found several links.

- http://study.mokoron.com/ - annotated tweets
- http://text-machine.cs.uml.edu/projects/rusentiment/ - some more posts from VK
- https://github.com/dkulagin/kartaslov/tree/master/dataset/emo_dict

Russian SQUAD
- https://github.com/deepmipt/DeepPavlov/blob/0.0.9/deeppavlov/dataset_readers/squad_dataset_reader.py#L43

Happy holidays!

#nlp

Environment setup for DS / ML / DL

Some time ago made a small guide for setting up an environment on a black Ubuntu machine.

If works both for CV and NLP.

If you like this, please tell me, I will add newer things:
- nvtop;
- CUDA10 with PyTorch 1.0;
- Scripts for managing GPU fan speed;

http://github.com/snakers4/gpu-box-setup/

#deep_learning
#linux

Yet another repo with all possible pre-trained imagenet models

Now on 4 frameworks...
Looks too good to be true

https://github.com/osmr/imgclsmob

#deep_learning

Spark in me 2018 annual retrospective

TLDR:
- My personal progress and some views;
- ML is still amazing, but there are no illusions anymore;
- Telegram is still amazing, but commercialization looms;
- FAIR is an inspiration;
- Imcinnes with UMAP and HDBSCAN as well;

https://spark-in.me/post/2018

ЗЫ
Еще написал немного по-русски, немного со спецификой, если вам так удобнее

https://tinyletter.com/snakers41/letters/spark-in-me-2018

#data_science
#deep_learning

Happy holidays to everyone)

Linux subsystem in Windows 10

It works and installs in literally 2 clicks (run one command in Powershell and then just one-click install your Linux distro of choice in Windows Store (yes, this very funny indeed))!

Why would you need this?
To make and backup files on one command for example =)

Something like this becomes reality on Windows:

cd /mnt/d/ && \
TIME=`date +%b-%d-%y` && \
FILENAME=working_files_tar-$TIME.tar.gz && \
INCREMENTAL_FILE=backup_data.snar && \
echo 'Using folderlist' $FOLDERS && \
tar -czg $(<folders_backup.txt) --listed-incremental=$INCREMENTAL_FILE --verbose -f $FILENAME

Also, you may add rsync or scp and you are good to go!

Also other potential use cases:

- You are somehow vendor locked (I depend on proprietary drivers for my thunderbolt port to attach an external GPU) or just are used to Windows' windows (or are just lazy to install Linux);
- You need one particular Linux program or you need to quickly test something / do not want to bother replicating your environment under Windows (yes, you can also run Docker, but there will be some learning curve);
- You run all of your programs remotely, and use your Windows machine as a thin client, but sometimes you need git / bash / rsync - i.e. to download movies from your personal NAS;

#linux

Using nargs

Wrote about this a year ago.
Forgot about it, a friend reminded me.
You can pass lists to the python command line arguments.

parser.add_argument('--classifier_conf', default=[512, 2048, 5005], nargs='+', type=int)

and then just add params to your call as follows

--classifier_conf 512 2048 5005

#deep_learning

First 2019 DS / ML digest

No particular highlights - just maybe ML industrialization vector is here to stay?

https://spark-in.me/post/2019_ds_ml_digest_01

#digest
#deep_learning
#data_science