For fantasy books, I don't even try to do it. After reading 100+ books I don't even try to remember them. Usually I remember the most enjoyable moments and my emotions - this is enough for me.

As for non-fiction, sometimes I copy quotes that are interesting to me and reread them once in a while.

I divide the links into 3 groups:

• relevant to what I'm doing currently - I open and read them;
• possibly interesting - for example, I'm interested in NLP, CV, not interested in time-series or audio. I save this links into relevant folders;
• not in my area of interest - ignore them;

First of all, it is important to understand that we can't keep up with everything. There are too many things happening around us to be able to know all of them.

That being said, I'm subscribed to the following newsletters:

• Data Elixir
• Data Machina
• DataScienceWeekly

They cover most of the advances, I think.

Yes! They are amazing. These were the first books ever that I bought in such a premium edition, and they were worth it.

A leatherbound edition of the first two Stormlight Archive books (from the Kickstarter)!

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P. S. This is a bit different, but I also really like reading on Kindle with a good Sleeve Cover, it feels like reading a book in a premium edition.

I read multiple self-help books but didn't like most of them: they often could be condensed into several pages, or the ideas are quite trivial. But there were several of them that I appreciate:

• Rise: 3 Practical Steps for Advancing Your Career, Standing Out as a Leader, and Liking Your Life by Patty Azzarello. This is a good book with general guidelines for being successful in corporate work: set priorities and complete your work with excellence, build credibility and develop your network;
• So Good They Can't Ignore You: Why Skills Trump Passion in the Quest for Work You Love by Cal Newport. The idea is in the title;
• Deep Work: Rules for Focused Success in a Distracted World by Cal Newport. The book about the importance of Deep Work and flow state for achieving great results;

It it 4-7 mlns of rows. Sometimes I hit memory limits when I try to create ~100 columns or when calculating complex rolling features. Also, I have multiple programs running - like Pycharm, so they contribute to the problem.

It depends on your data. If your datasets aren't large, it will be fine.

At my job, we were issued Macbooks pro M1 with 16 GB RAM. Sometimes (but not often) I hit the limits of the RAM.

Right now neural nets are matrix multiplications and activation functions...