This document discusses PyData, a meetup group for data science. It provides reasons to join PyData like its community and for learning machine learning software. It also includes links to Kaggle competitions and tutorials related to toxicity analysis and natural language processing. The document advertises Microsoft's machine learning and AI products and when each may be suitable. It ends by promoting upcoming meetup events and linking to tutorials on deep learning techniques like GRUs, LSTMs and word embeddings.

1. anvykhod@Microsoft.com
https://www.meetup.com/PyData-Slovenia-Meetup/events/248301554/

2. PyData: https://www.meetup.com/pro/pydata/

4. • Best and fastest learning
• Great community
• If you want to really try an ML
software product, this is the
way
• Like sports compared to real-
life
Reasons to

6. ▪
▪
▪
▪
▪

7. https://www.kaggle.com/jagangupta/stop-the-s-toxic-
comments-eda
https://www.kaggle.com/vykhand/toxic-fortune-cookie

8. https://www.kaggle.com/jagangupta/understanding-the-topic-of-
toxicity
https://www.kaggle.com/sergeifironov/eli5-for-
toxic?scriptVersionId=2595250
https://www.kaggle.com/arthurtok/spooky-nlp-and-topic-
modelling-tutorial

11. https://github.com/vykhand/kaggle-jigsaw
Everything is a hyperparameter!

21. Demo

22. Demo

23. https://Aka.ms/dsvm
PyImageSearch reviewhttps://www.pyimagesearch.com/2018/03/21/my-review-of-microsofts-deep-learning-
virtual-machine/

24. Azure Machine Learning
Studio
(GUI interface, R, Python,
operationalization through webservices,
Azure only)
Azure Machine Learning Services
(code-first, Python (R comming), model
management, runs locally, in docker – Azure
/on-prem, use your own IDE, operationalize
through webservices)
Machine Learning Server
(on-prem & Azure**, license SQL Server,
R/Python, pushdown R/Python to
Hadoop/Spark, SQL Server,
Operationalization module – webservices,
interface R/Python, use your own IDE)
SQL Server Machine Learning
Services
(on-prem & Azure*, license SQL Server,
R/Python inside Stored Procedure,
operationalization SQL, interface SQL,
models stored in table)
ON-PREMISES
A ZURE
DATA SCIENTISTSBUSINESS
POWER USERS

25. Machine Learning & AI Portfolio
When to use what?
What engine(s) do you want
to use?
Deployment target
Which experience do you
want?
Build your own or consume pre-
trained models?
Microsoft
ML & AI
products
Build your
own
Azure Machine Learning
Code first
(On-prem)
ML Server
On-
prem
Hadoop
SQL
Server
(cloud)
AML services (Preview)
SQL
Server
Spark Hadoop Azure
Batch
DSVM Azure
Container
Service
Visual tooling
(cloud)
AML Studio
Consume
Cognitive services, bots
(cloud)
Data Lake
Analytics

28. http://sacred.readthedocs.io
where

32. Demo

33. https://www.kaggle.com/jagangupta/lessons-from-toxic-blending-is-the-new-sexy

34. Understanding GRUs
Understanding LSTMs
Deep learning for NLP best practices
Sentiment analysis for twitter with hierarchical
attention on DSVM

39. https://buhrmann.github.io/tfidf-analysis.html

40. Glove
Fasttext
Good overview article
https://blog.acolyer.org/2016/04/21/the-amazing-
power-of-word-vectors/
https://www.depends-on-the-definition.com/guide-to-
word-vectors-with-gensim-and-keras/
https://radimrehurek.com/gensim/models/word2vec.html

44. Demo

45. 1st place
3rd place
5th place
34th
27th
Neptune team