First 2019 DS / ML digest

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

spark-in.me/post/2019_ds_ml_digest_01

#digest

#deep_learning

#data_science

2019 DS/ML digest 01

2019 DS/ML digest 01 Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me

Someone implemented instance weighted CE loss for PyTorch

gist.github.com/nasimrahaman/a5fb23f096d7b0c3880e1622938d0901

#deep_learning

Pytorch instance-wise weighted cross-entropy loss

Pytorch instance-wise weighted cross-entropy loss. GitHub Gist: instantly share code, notes, and snippets.

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

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

Happy holidays to everyone)

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;

spark-in.me/post/2018

ЗЫ

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

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

#data_science

#deep_learning

Spark in me - annual retrospective 2018

Spark in me - annual retrospective 2018 Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me

Yet another repo with all possible pre-trained imagenet models

Now on 4 frameworks...

Looks too good to be true

github.com/osmr/imgclsmob

#deep_learning

osmr/imgclsmob

Sandbox for training large-scale image classification networks for embedded systems - osmr/imgclsmob

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;

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

#deep_learning

#linux

snakers4/gpu-box-setup

Contribute to snakers4/gpu-box-setup development by creating an account on GitHub.

Sentiment datasets in Russian

Just randomly found several links.

- study.mokoron.com/ - annotated tweets

- text-machine.cs.uml.edu/projects/rusentiment/ - some more posts from VK

- github.com/dkulagin/kartaslov/tree/master/dataset/emo_dict

Russian SQUAD

- github.com/deepmipt/DeepPavlov/blob/0.0.9/deeppavlov/dataset_readers/squad_dataset_reader.py#L43

Happy holidays!

#nlp

gist.github.com/lucidyan/4359b5973e5c3cee818595734c0ab7a9#gistcomment-2794677

Prevent NVIDIA GPUs' throttling on headless server

Prevent NVIDIA GPUs' throttling on headless server - gpu-control.md

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

Practical creepiness

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

#deep_learning

Spell-checking on various scales in Russian

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

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

#nlp

Исправляем опечатки в поисковых запросах

Наверное, любой сервис, на котором вообще есть поиск, рано или поздно приходит к потребности научиться исправлять ошибки в пользовательских запросах. Errare...

www.facebook.com/nipsfoundation/videos/203530960558001

Neural Information Processing Systems

Welcome to NeurIPS 2018 Turorial Sessions. This tutorial on Visualization for Machine Learning will provide an introduction to the landscape of ML visualizaions, organized by types of users and their...

DS/ML digest 32

Highlights:

- A way to replace softmax in NMT;

- Large visual reasoning dataset;

- PyText;

spark-in.me/post/2018_ds_ml_digest_32

#digest

#deep_learning

#data_science

2018 DS/ML digest 32

2018 DS/ML digest 32 Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me

ganbreeder.app/

A collaborative tool for discovering images.

PyText

- PyText 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

facebookresearch/pytext

A natural language modeling framework based on PyTorch - facebookresearch/pytext

www.youtube.com/watch?v=ZKQp28OqwNQ

BigGANs: AI-Based High-Fidelity Image Synthesis

PyText?

NLP library build on top of PyTorch 1.0 by Facebook?

- No repo link though (github.com/facebookresearch/pytext)

- The paper also mentions the same limited API as AllenNLP has ... =(

research.fb.com/publications/pytext-a-seamless-path-from-nlp-research-to-production/

facebookresearch/pytext

A natural language modeling framework based on PyTorch - facebookresearch/pytext

youtu.be/kSLJriaOumA

A Style-Based Generator Architecture for Generative Adversarial Networks

Simpsons paradox

Nice explanation

towardsdatascience.com/simpsons-paradox-and-interpreting-data-6a0443516765

#data_science

Simpson’s Paradox and Interpreting Data

The challenge of finding the right view through data

DS/ML digest 31

Highlights of the week:

- PyTorch 1.0 released;

- Drawing with GANs;

- BERT explained;

spark-in.me/post/2018_ds_ml_digest_31

#digest

#deep_learning

#data_science

2018 DS/ML digest 31

2018 DS/ML digest 31 Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me

PyTorch 1.0 release

View Release:

 github.com/pytorch/pytorch/releases/tag/v1.0.0

#deep_learning

pytorch/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch

youtu.be/AGm3hF_BlYM

This AI Learns Human Movement From Videos

Painting with GANs

This looks just awesome.

I guess it will not work in real resolutions yet.

gandissect.res.ibm.com/ganpaint.html?project=churchoutdoor&layer=layer4

#deep_learning

Painting with GANs from MIT-IBM Watson AI Lab

This demo lets you modify a selection of meaningful GAN units for a generated image by simply painting.

This kind of mirrors my own old post

spark-in.me/post/epistemic-responsibility

Эпистемологическая ответственность

Почему мы должны нести ответственность за идеи, в которые верим и которыми пользуемся Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me

A cheeky ML/DS themed sticker pack for our channel

Thanks to @birdborn for his art.

You are welcome to use it:

t.me/addstickers/ML_spark_in_me_by_BB

If you would like to contribute / create your own stickers - please ask around in our channel chat.

#data_science

Channel Edit Photo

Article about the reality of CV in Russia / CIS

(RU)

cv-blog.ru/?p=253

Also a bit on how to handle various types of "customers", who want to contract CV systems from you.

Warning - too much harsh reality)

#deep_learning

DS/ML digest 30

spark-in.me/post/2018_ds_ml_digest_30

#digest

#deep_learning

#data_science

2018 DS/ML digest 30

2018 DS/ML digest 30 Статьи автора - http://spark-in.me/author/snakers41 Блог - http://spark-in.me