Matteo Latinov

[Mark Tenenholtz @marktenenholtz]

21 thoughts any data scientist should read to succeed in the real world (that I learned the hard way):

1. Go talk to the person that will use the model before you start building it. You'll waste a lot less time.

(read on)

[Matteo Latinov @LatinovMatteo]

@svpino I like random forests as well, more than decision trees which I'd say can easily overfit the data

[Matteo Latinov @LatinovMatteo]

@marktenenholtz Good resources for learning timeseries forecasting?

[Matteo Latinov @LatinovMatteo]

@marktenenholtz Thoughts on mutual information for feature selection?

[Matteo Latinov @LatinovMatteo]

@marktenenholtz Problem decomposition

[Roger Federer @rogerfederer]

To my tennis family and beyond,

With Love,
Roger

[Jordan Feigenbaum, M.D. @Jordan_theCoach]

Fear of injury is a major reason people don’t lift weights despite a very low injury incidence of 2-4 injuries/1000 hours. People on social media who portray themselves as experts raising concerns of injury due to technique despite the lack of evidence are part of the problem.

[Matteo Latinov @LatinovMatteo]

@xLaszlo Yes absolutely, domain specific names for the features. I see, so you could break up the dataset and still have the various features (now belonging to different classes) linked via the common id of the entity (row in this case)

[Matteo Latinov @LatinovMatteo]

@xLaszlo how would you manage a dataclass with 100 attributes (e.g. dataset with 100 features)? Would you explicitly list all features in the dataclass and extract/assign each with .itertuples()? So in this example: engagements, replies, ... , feature100

[Santiago @svpino]

One of the most common problems in machine learning:

How do you deal with imbalanced datasets?

Not only does this happen frequently, but it's also a popular interview question.

Here are seven different techniques to deal with this problem:

1 of 14

[Mark Tenenholtz @marktenenholtz]

I start every ML project by creating these files, more or less in this order:

1. .gitignore/README.md
2. eda.ipynb
3. data_notes.md

Days later...

4. baseline.(py/ipynb)
5. https://t.co/W9EVm4QIk9
6. https://t.co/ssL5g4E2CJ
7. error_analysis.ipynb

Structure -> creativity

[Barbell Medicine @BarbellMedicine]

#Sunday💤day - Check out our latest #podcast on #OSA! An excellent complement to our other work covering this incredibly important topic!

https://t.co/PSwHOj1YCP https://t.co/V1C6PP5tPE

[Mark Tenenholtz @marktenenholtz]

Last day to get this for $15!

Goes up to $25 tomorrow!
https://t.co/JNUNPEVWOH

[Matteo Latinov @LatinovMatteo]

@marktenenholtz Really enjoyed the course! Inspirational and straight to the point. I'll be looking to apply this framework from here on out. Well worth the money IMHO.
5 stars were given😀

[Barbell Medicine @BarbellMedicine]

🔥#SundayFunday🔥 Burning heel pain got you down? It might be #plantar #fasciitis! Read this quick and informative article from @DMilesPT who educates us on heel pain, how to #rehab and how to #recover! https://t.co/5J9y88kR6A

[Michael Nielsen @michael_nielsen]

The use of spaced repetition memory systems has changed my life over the past couple of years. Here's a few things I've found helpful:

[Jeremy Howard @jeremyphoward]

This is a great rule from Google's machine learning guides:
https://t.co/uHNWoIsMJ1

[Barbell Medicine @BarbellMedicine]

💤#SundayFunday💤 Today we share a ���NEW⭐ article from @AustinBaraki featuring @NateGordonMD on the important topic of #SleepApnea: An incredibly common condition which can have serious consequences but is fortunately, very treatable! Check it out! 👇👇
https://t.co/H0SPaRBtsb

[Mark Tenenholtz @marktenenholtz]

My 6 step pipeline for training tabular models:

1. EDA
2. Simple baseline
3. Create+test evaluation setup
4. Simple model with simple features
5. Error analysis
6. Feature engineering

Repeat 5+6 until you run out of time!

[Matteo Latinov @LatinovMatteo]

@jeremyphoward Thank you! Any chance fastai will be available on Amazon Sagemaker Studio Lab in the near future?