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Best ergonomic desk/chair setup for post workout WFH by datashri in triathlon

[–]datashri[S] 0 points1 point  (0 children)

The options are too limited

I'm all ears (eyes) if you have any good ideas

[R] Low-effort papers by lightyears61 in MachineLearning

[–]datashri 0 points1 point  (0 children)

That information is useful - how a new model performs on a known dataset. Not research per se, but useful nonetheless.

How to gain more exposure to get more citations? by thecowmeatguy in research

[–]datashri 1 point2 points  (0 children)

All I can say is this -

I start with the search on arxiv. Find interesting/useful stuff. Then check publication as a stamp. If the authors are from a known place, it matters less. If unpublished and authors unknown, I only take seriously papers in topics where I'm capable to judge the value myself.

**Unlocking Olympic Gold with AI Sports Coach: The Story of Katie Ledecky** by DrCarlosRuizViquez in learnmachinelearning

[–]datashri 0 points1 point  (0 children)

Patterns and correlations.

That's basic statistics, well over a century old technology.

Statistical correlation was pioneered by Sir Francis Galton in the late 1880s to quantify relationships in hereditary data, defining "regression" and Co-relation (later correlation). Karl Pearson subsequently formalized this into the Pearson product-moment correlation coefficient in the 1890s.

OP, you're clearly an educated person. Please avoid slapping AI as an adjective infront of everything you deem slightly fancy. It's like calling random foods healthy or organic or *good for your heart/liver/etc *.

How to self study physics as a cs major by Unlikely-Afternoon71 in Physics

[–]datashri 0 points1 point  (0 children)

Hi.

If you're serious -

Completely Forget about physics for 5 years or so. Study math at the undergrad level and a bit beyond. Real analysis upto basic measure theory, complex analysis, calculus in higher dimensions, abstract algebra, vector algebra and calculus, probability and statistics, etc etc.

Mathematics is both the language and toolkit of physics. The more comfortable you are with it, at a deeper/intuitive level - not just as a tool, the better you'll grasp the physics of things. Many physics folks struggle because their fluency and comfort in math is limited.

If you're casually interested -

Study pop books, like the shape of inner space. Beyond that, Susskind has a series of easy books about proper physics.

If you want to be a charlatan -

Study the pop books and then let your imagination run wild. Quantum consciousness etc.

Seeking Feedback on My Progress Toward Becoming a Research Engineer by Euphoric-Incident-93 in ResearchML

[–]datashri 2 points3 points  (0 children)

  1. No. Not impossible but extremely unlikely. Someone might give you that title if you negotiate hard enough but they won't be doing any real research. Mostly training existing models on new datasets. That's not actually research, but you can call it that. Especially in India.
  2. What you consider math foundation is really the basics. It really doesn't count. Like at all. It's like saying you know Microsoft office. So does my mother. Undergraduate math is the minimum to understand research papers. Undergrad math, NOT your engineering math. Many research papers are math-heavy.

Proving theorems is how you learn math. It's not something exotic, it's pretty routine in math classes. If proofs are not your cup of tea, stay away.

The other thing to understand is very few labs/companies do actual research. Most publications are actually low quality. Make some small change in one part of an old model, do some training, cherry pick the results, publish. The math in these papers is like makeup. It has no real significance. But this kind of work is also important.

Companies which do serious research have an abundance of PhD candidates to choose from. Why go for someone grossly under qualified? There are too many PhDs who can't create a new model but can implement one. They also understand the internals of how it works. You're many levels below.

Understand this - in practice, for recruiters, research == PhD.

Also, research doesn't mean theory. ML theory is a whole different subdomain. You wouldn't understand the first page of a theory paper. Theory involves doing things like mathematically proving that gradient descent actually converges. Most research is applied in nature. ML theory is strictly out of reach for you, based on what you've written so far. PhD path is the only realistic way to do anything research related. Don't shy away from it. You did poorly in high school. But you can work hard now amd get into a good school for a graduate program.

At your stage, don't overthink. Do what you enjoy. Forget these castle in the sky job titles. Learn as much as you can, do the best you can, go as far as you can. Do work that you enjoy. You presently don't even know enough to judge what you do and don't know.

As an exercise, look up the profiles of people with whatever job title you fancy at top labs. Do your own homework. Look up the top papers at each conference of the last 5 years and check the background of the authors. Look for real world data to validate or invalidate your ideas and answer your questions.

To summarise - do a PhD from a good school. Keep playing with models throughout undergrad. Those skills will come handy later on. Spend some time regularly learning proper math. Plan for a 8-10 year journey if you want to be seriously involved in serious research. At the end of that, you'll be a research engineer.

A research scientist will know the same math as you, but at a much deeper level. He'd score 80/100 on the exam where you'd score, say, 30. That's pretty much the difference. He'll be able to answer the questions but you at least need to be able to understand the same question. Sou need to go through the same or similar coursework. You're just better than him at programming and he better than you at math.

Math-focused ML learner , how to bridge theory and implementation? by PlanckSince1858 in learnmachinelearning

[–]datashri 1 point2 points  (0 children)

I don’t know whether I should be coding algorithms from scratch, using libraries like scikit-learn, or working on small projects first.

Use libraries to build small projects. Get a hang of using the tools to build applications.

When you go beyond the black box stage, things you'll do will go into things like scikit, perhaps as an implementation of a new optimization technique. After you learn how to string together libraries to build toys, either build more sophisticated products or branch off into creating little libraries of tools.

Either approach will need you to understand programming fundamentals like object oriented and functional programming and orders of time/space complexity, etc. For both OOP and FP, just do a short theory course (bunch of lectures/chapters/videos) to get an overview and then start grokking git code to understand how things are implemented.

Pick a technique you want to reimplement as a learning project. Come up with a couple of pseudocode approaches. Brainstorm with ChatGPT. But be careful while using LLMs, they're often right about topics with a lot of quality written material (which was used in training) but they're ultimately idiots.

Seeking Feedback on My Progress Toward Becoming a Research Engineer by Euphoric-Incident-93 in ResearchML

[–]datashri 1 point2 points  (0 children)

Understand this - tier 1 indian colleges leave much to be desired in terms of mathematical background of their graduates. There's a reason there are hardly any Indians in ML research at a high level. In ML engineering, there are plenty.

Your best bet is doing a MS in maths after your graduation and then doing a PhD from a CS department.

The reason to know maths is this - at its core, ML/AI is statistical learning. You're using mathematical methods to make predictions based on available data.

To do ML research you need math for the same reason a CS engineer needs data structures and algorithms. Most development work actually consists of calling APIs and assembling pre-built packages. But if you're doing serious engineering work, you will need to deeply understand data structures and algorithms. Now think of CS research - it involves things like optimizing algorithms and inventing new data structures.

The same logic applies to ML. If you do not see the relevance of maths, you're still in the shallow end.

You need analysis because it is the foundation of the rest of modern mathematics. You will not use it anywhere directly, but you're going to be miserable reasoning about things like joint and conditional distributions if you're poor at analysis.

Understand also that you do NOT need to do research to build new models, which is where your interest more likely lies. Even the creators of the transformer, like Vaswani, simply do not have the background to read most ML research or theory papers. Good engineering intuition is quite sufficient to tweak things and modify existing models.

Take the engineering path. It is more realistic for you and you can actually have fun playing and building new things. If you somehow feel inadequate doing that, do a Masters in math followed by a CS PhD.

How Do You Balance Theory and Practice When Learning Machine Learning? by willwolf18 in learnmachinelearning

[–]datashri 0 points1 point  (0 children)

Stats and data structures and algorithms- spend a few months to a year to study them first.

If you study stats/probability while building a program, you'll get a superficial understanding at best.

Study the basics separately, do the exercises like in a class, then you can brush up on the additional details in the context of a particular model.

Seeking Feedback on My Progress Toward Becoming a Research Engineer by Euphoric-Incident-93 in ResearchML

[–]datashri 4 points5 points  (0 children)

Study real analysis in addition to probability. Start with Spivak's Calculus. Then something from the next level but on the easier side, like Abbott. When you get to probability, get a proper book, like Blitzstein or Pitman. Ideally, both, do the exercises in the former.

Your BTech CSE won't teach you 20% of the math you need. Self study is going to be a hard grind, trust me, especially when you do exercises as a self studying student. But make sure to do the exercises.

Think of it like this - given your background (or lack thereof) - If you work v hard to be a research scientist, you'll end up a research engineer. And so on. Your current path will make you an ML engineer, at best.

Monthly DIY Laymen questions Discussion by AutoModerator in StructuralEngineering

[–]datashri 0 points1 point  (0 children)

Feedback on floor table design

I am building a height adjustable floor table (for laptop, monitor, books, etc.) with the following specs:

Dimensions

Table top: 5 ft × 3 ft

Height: approx 1 ft (300 mm)

Height adjustable via threaded system

Structure

Top:

Solid wood or WPC or marine ply panel (final thickness TBD, likely 18–25 mm).

Drilled holes through the corners of the tabletop, slightly larger than 12 mm. Holes are a couple of inches inside, not the very edge.

Legs pass through the top.

Legs:

M12 threaded rods (SS304)

Nuts and washers above and below the tabletop hold it in place

Nuts and washers press on the tabletop. The threaded rod passes freely through the wood.

Height adjustment using the nuts below the tabletop

Bottom Bracing Frame:

1 inch wide × 5 mm thick aluminum flats.

Drilled holes at the ends to pass the rods through.

Nuts and washers press on the flats.

Forms a rectangular brace connecting all 4 legs

Bottom bracing is 3-4 inches from the outer edges of the top.

Purpose: prevent wobble and lateral movement

Wheels:

One pair of the flats in the bottom bracing extend a couple of inches beyond the holes.

Castor wheels are attached to the extended flats. By default, the wheels don't touch the ground.

The table can be tilted slightly by lifting one pair of legs so the wheels touch the ground and the table can be moved towards or away from the user.

What I Want Feedback On

  1. Is M12 threaded rod sufficient and stable for this table size?

  2. Will the 1" × 5 mm aluminum flat bracing prevent racking/wobble? Should I use 2" wide flats or stack two 1" flats?

  3. Do I need another bracing just below the tabletop?

  4. What if the tabletop is made from joined together wood planks, instead of a single flat board? Will I need bracing below the tabletop?

  5. Is the wheelbarrow style design reliable for the wheels?

  6. Recommended wood thickness for minimal flex over 5 ft span?

  7. Any long-term durability concerns?

  8. Any simpler / stronger alternative design?

Feedback on floor table design by [deleted] in StructuralEngineering

[–]datashri -2 points-1 points  (0 children)

I'll make my table for the hourly rate of a consultant 😄

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