@ElonMast nothing wrong with some free internet points. Atleast I know someone got value out of this.

Analyst

Not doubting the value link has, but really curious to know about grapes accumulation.

Product Manager

Did you actually?  
 Based on your previous posts in this community, I am starting to doubt your credibility.  
 I could be wrong.

Yeah focused too much on accumulating grapes.i share the research paper findings as well. 
 But I don't share it on grapevine , because people can easily find my identity via LinkedIn since I started to post weekly there.

Machine Learning Engineer

Data Scientist

So every time you add these resources, could you please add the questions that were asked pertaining to that resource? It’d be really beneficial for the community.  
 Thanks!

For which role at gs ? Data scientist do they hire data scientist too?

Can I DM, looking to crack DS role but not encouraging results. 
  
 Have a mix of DS and analytics experience

we finally got someone from DS community being this active and sharing such helpful resources. 50 grapes no big deal 🤝💯

Senior Data Engineer

Curious about your role at GS 
 Is it more on the statistical modeling side or GenAI side? As it is a financial institution

What do ML engineers do at investment banks like GS?

Hello, looking for a referral at Goldman Sachs.

Business Analyst

I have been getting great response on my posts lately so decided to share one of the most important books on Optimization algorithms that helped me crack my interview at Goldman. Mykel J. Kochender...

Welcome to Data Scientists's Private Community

I have been getting great response on my posts lately so decided to share one of the most important books on Optimization algorithms that helped me crack my interview at Goldman. Mykel J. Kochenderfer and Tim A. Wheeler have done a greta job explaining some of the toughest concepts in Mathematical optimization and they have implemented the code in Julia which is super fun to code along.

Next post on AI will come when this post gets 50 likes.

Sharing the link to the book here, it covers some of the most foundational topics in optimization.

This book provides a broad introduction to optimization with a focus on practical algorithms for the design of engineering systems. We cover a wide variety ...

Algorithms for Optimization

This book helped me crack my ML interview at Goldman Sachs...

This book helped me crack my ML interview at Goldman Sachs...- Grapevine

Welcome to Software Engineers's Private Community

AMA about Algorithmic Trading

Quant Trader

Welcome to Indian Startups's Private Community

Hello Everyone, I’m starting new position as ML Engineer, mostly I’ll be working on GenAi integrated solutions and have to work on complete end to end architecture.
is there any suggestions for me, 
is there something you wished someone would’ve told you/ guided you when you were starting, and mistakes to avoid, approaches or paths that worked for you, and resources (books, blogs, videos) which helped you a lot.

Suggestions you wished someone gave you when you’re starting as ML Engineer

Welcome to IT Company Discussion's Private Community

Few people asked me for my recommendations on Tech resources -

I'll divide across following sections -

1. CS Fundamentals - Its a recommended flow that you should know beyond knowing how to code. 
Follow this - https://teachyourselfcs.com/

2. DSA -
a) https://neetcode.io - Its blind 150 is awesome. Its paid courses are also decent
b) CLRS book (How can we leave that?)

3. System Design resources -
a) System Design interview Vol 1 and 2 - Alex Xu OR you can take annual subscription of their website - https://bytebytego.com
b) The OG book - DDIA - this is a must read and has very high ROI

4. Backend Engineering - 
- Learning Backend development like a curriculum - https://boot.dev
- Coding challenges 
a) For beginners to experienced - https://codingchallenges.fyi/challenges/intro
b) For experienced folks - (codecrafters.io) https://app.codecrafters.io/r/adorable-eagle-556273 (Referral)

5. Software Engineering -
What Software Engineering truly is? - Software Engineering at Google Book(Oreilly)

6. DevOps / AWS -
a) AWS course - https://learn.cantrill.io/ - To learn more in depth than just passing certifications
b) Other DevOps resources - kodekloud.com (all devops tools, especially kubernetes ones are good)
c) What SRE truly is? - Site Reliability Engineering book (Oreilly)

7. Promotions / Impact / Growing in the role -
a) JoinTaro - Website where you get answers to your questions about promotions, impact etc. https://www.jointaro.com/r/sarvajs408/ - Check the founders posts on linkedin on what to look for
b) Software Engineering Handbook by Gergely

7. Misc / Good to read -
a) Head first series by Oreilly publication (Design Patterns, OOAD)
b) Chaos Engineering (Oreilly)

If you guys think we can add more things, where you know the resources are absolute best to be recommended for the return on investment(time, money) they give, please do drop in your recommendations in the comments

Software Engineering Resources Recommendations

Consultant

Hey guys, 
I want to revise my ML & DL concepts and would like to know if anyone knows good references/sources to revisit the concepts
I want to cover the algorithms in fundamental depth rather than just application of models
(
ex : difference between gradient descent / ols  regressions
When to use beta in classifications when looking at f score etc. 
what is the difference between acf/pacf in time series 

Can someone suggest good sources where I can go through such concepts in depth?
)

Fundamentals of ML&AI

Hey everyone, 
I have one doubt regarding the skillets of data scientists. Is this necessary to having the knowledge about MLOps for data scientists ??
cz I'm doing course of full stack data science course from ineuron and now they are teaching MLOps. 

Thank you.

MLOps for Data Scientist

Recently I completed "Designing Data-Intensive Applications by Martin Kleppmann" (in almost a year). Book is highly recommended.

Share your recommendations

Which books are your favorite?

Software Engineer

Please like and bookmark this if you find it useful. I have acess to the top AI researchers and ML PhDs working on the cutting edge in India and the US, will post some good content if this crosses 100 likes.

Ilya Sutskever, OpenAI cofounder, gave John Carmack this reading list of approximately 30 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today.’

FYI: John Carmack is also considered to be the greatest programmer of all time.

Ilya 30u30

OpenAI Cofounder: "Learn these 30 research papers and you will know 90% of what matters today."

Welcome to Product Managers's Private Community

This is not a question for the cocky new grad in the overvalued startup, but for the few seasoned PMs that still reply out here.

Where do PMs go after they peak?

By peaking I mean arriving at a GPM or Staff PM position at the top 10-15 companies (you can do your own ranking here).

Nobody has the true statistics but I will use the assumption that 75% (debatable) of those who peak, will get laid off within 2 years from their ‘peak role’. The remaining will be able to progress into DoP and VP in the same top company or another in the top 15.

Where do they go then?

I am pretty positive, from experience, that they don’t go any up.

Do they go down the ladder of new startups for titles and ending up in the worst ones after being laid off 4-5 times? Do they quit all together?

Do they open a farm?

Do they start their own startup?

Comments and thoughts folks!

Where Do PMs Go After They Peak?

Software Developer

I'm a PM with 2.5+ years of experience. I have a habit of reading product management newsletters. But now I'm planning to start with good product management books. 

Suggest a book that you've read and was practically useful in your work. This can help folks like me facing this issue of choosing between an ocean of books out there.

Suggest the best PM book you've ever read

YOE - 5 Profile - business Analyst, good with SQL, average with python and ML concept, but worked on python and ML in my previous company. 
Requesting advice to get into machine learning or gen ai profile. Courses or anything else that would help. I understand it's a 6 months to one year journey.... Anyone who has transitioned recently kindly help.
Reason to move - better opportunities and jobs moving towards Gen Ai.

This book helped me crack my ML interview at Goldman Sachs...

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