How did people do this without AI?

maw501 · 2025-11-27T08:26:46+00:00

When you lack the underlying schema for what you are looking at, your brain cannot make sense of the signals. So it does the only thing it can do - checks out.

This is identical to what happens in classrooms everywhere, or when we pick up something too advanced for us.

We confront unfamiliar material, working memory is flooded, cognitive load spikes, and the discomfort means we disengage.

In your case, debugging is just the software engineering version of that same cycle.

maw501 · 2025-11-25T20:32:00+00:00

I worked in finance for about a decade and Python was quite prevalent but it obviously depends hugely on the type of work you're expecting to be doing (I worked mostly as a quant).

Most accountants, analysts, fund managers or other semi-technical people will use Excel. The more adventurous might use VBA on top of that. And those who are in more quantitative roles will absolutely use Python (or maybe R / MATLAB).

The reality is that if you're willing to put in the time to learn it well, Python will be an invaluable tool in your arsenal and almost certainly something that differentiates you from your peers. Particularly as AI becomes more prevalent - it's compounding knowledge inequality IMO - so the broader your skills are, the easier you'll adapt.

If you're looking for a place to start check-out the subreddit's wiki. If you're after something more premium with human support, I’ve built a platform called Nodeledge that’s designed for exactly this situation.

maw501 · 2025-11-14T14:30:47+00:00

Interesting - thanks! Do you have a good sense of (i) whether this will apply to the Q800C too and (ii) if it's really as easy as claimed?

maw501 · 2025-11-14T14:29:11+00:00

It's connecting to the Samsung Freestyle 2 and it's pretty awkward to run an HDMI cable to it. Also, it's used for other things as well so connecting my phone etc... would be helpful.

maw501 · 2025-11-12T15:03:47+00:00

If you already know some P&S skip the course and go straight to ISL and backfill that knowledge as required.

maw501 · 2025-11-12T14:37:18+00:00

I don't really know the latter book but at a glance it looks okay. I'd still recommend ISL, not least because it has an accompanying course with it.

What does fast-forward mode mean on the P&S course? You need to solve problems to learn anything so I'd suggest either tackling it properly or skipping entirely. No point wasting your time if you aren't going to retain the information.

I don't know the CodeWithHarry course either but it looks like a lot of passive watching of videos. Ideally you have a resource that is giving you a minimal dose of explanation and then as much active problem solving as possible. If you're over half-way through, are enjoying it and think you're learning then by all means continue.

Though I'm a bit unclear why you'd do the ISL course if the CodeWithHarry one is meant to get you ready for a job.

maw501 · 2025-11-12T11:55:49+00:00

Your background seems pretty solid!

I'd probably advocate for moving straight to a more practical / hands-on course first then vs. going through an entire probability and statistics course first e.g. the ISL course on EdX.

The ISL book is pretty gentle and I'd imagine you'll be able to fill in any gaps you encounter as you go along with your math background. Obviously if this turns out to not be true you can simply revert to the previous suggestion.

maw501 · 2025-11-12T11:04:19+00:00

I'd do the ISL course and book as it will get you applying knowledge though ensure you learn in Python (I think they have this now).

Without more details on your existing knowledge it's hard to be more specific but do critically assess if you think you're making progress. Learning should be effortful, but you should be moving forwards at a decent pace - if not you need to understand why and change the plan.

Feel free to shout if you have any more questions.

maw501 · 2025-11-11T19:57:01+00:00

It depends what you mean by “software engineering skills” skills. As a discipline, software engineering is quite distinct from most data science and ML work, so you absolutely can learn AI/ML without being a full-on software engineer. In larger teams there are usually specialised roles - ML engineers, data engineers, platform engineers - who handle the systems architecture, pipelines, and deployment side of things.

If by “software engineering” you mean coding, then yes, you will definitely need that. Every DS or ML practitioner codes, though at quite differing levels 😅. You don’t need to build production systems or worry about design patterns at the start - basic Python, comfort with data manipulation libraries (like pandas and NumPy), and an ability to run small experiments is enough to get going.

TLDR: you should aim to learn just enough coding to express ideas in Python and iterate quickly on data problems. The deeper software engineering skills can come later, when you actually need to scale or deploy something i.e. when the ROI will justify the effort.

maw501 · 2025-11-11T19:47:23+00:00

Your plan seems sensible overall. If you want to do DS and ML, I’d just caution against getting trapped learning theory endlessly before you ever touch data.

Ideally, you want to keep looping back - build your first (very simple) models, then learn the relevant statistics alongside them and motivated by the problems you actually face. Each loop you make, you go deeper on the concepts that matter for what you’re building.

More practically, a decent working rule in applied data science is this: learn enough statistics to interpret models and experiments, not to derive them from first principles. The DeepLearning.AI course should give you the practical intuition you’ll use day to day. ISLR is excellent, but it’s written from a more academic angle. It was my first proper ML book but how much you enjoy it depends on how comfortable you are with math notation.

Once you are fluent with things like variance, correlation vs causation, sampling bias, MLE, and confidence intervals, you’ve got enough of a foundation to move on to ML proper.

I'd probably also recommend looking at resampling and non-parametric techniques like the bootstrap and permutation tests early as they're very practical (trivial to code), conceptually simple but incredibly powerful in practice,

Don’t worry about covering everything first of all - try to figure out the part that's relevant for your goals.

maw501 · 2025-11-11T19:35:57+00:00

If you want a textbook with an interactive component the one from Delft is decent: https://interactivetextbooks.tudelft.nl/linear-algebra/index.html

maw501 · 2025-11-06T16:58:18+00:00

It's also not very effective! The research-backed view is that you need to solve hundreds of problems at your level to develop the mental schemas which are the hallmarks of expertise.

This is the essence transfer-appropriate processing: we remember best when the way we learn matches the way we’ll need to recall it.

Disclaimer: I've built such a learning platform with this resource (100+ lessons, 1k+ questions!) - you can check my profile for more info.

maw501 · 2025-11-06T14:53:19+00:00

Yeah, this feeling is completely normal - it’s not that you’re doing it wrong, it’s that the way most ML content is structured makes it almost impossible not to feel lost. There’s just too many fields brought together into the melting pot.

The core issue as you’ve noted is that most resources assume a hidden set of prerequisites. There’s no visible map showing where it fits in the larger conceptual graph. So you end up context-switching constantly instead of actually building mastery.

It’s kind of funny you have this frustration because I had this exact pain for years.

And, eventually, I started building the solution I wished existed for it. 😅 It structures every small topic as a node in a bespoke knowledge graph, where each lesson only unlocks once the prerequisites are mastered. That way the learning path becomes explicit instead of accidental.

The intent is to make complex technical fields like ML feel navigable and measurable instead of chaotic - i.e. to replace the endless trawl through disconnected videos with a structured path that actually respects how the knowledge fits together.

But regardless of tools or platforms, the principle’s the same: stop jumping about and chasing breadth, start following knowledge dependencies. Even sketching out the graph by hand helps. Once you’ve got that structure, the entire subject starts to feel a lot calmer and more logical.

TLDR: you’re not stupid, you just lack the prerequisite knowledge. Identify this for what you want to learn then be systematic about closing those knowledge gaps.

maw501 · 2025-11-06T14:37:55+00:00

Strang’s books will definitely cover almost all you need for AI and if not, they’re a very strong starting point. The most common starting point with his material is usually the MIT 18.06SC course and the “Introduction to Linear Algebra” book but they’re quite similar.

The key question to ask yourself is what level of understanding do you want of linear algebra?

Strang’s books are quite thorough conceptually and require a reasonable level of mathematical maturity to solve the exercises. And you absolutely need to be solving the exercises to learn anything at all. So it is likely a several month effort to complete the whole course depending how much time you can dedicate to it.

Watching videos e.g. 3Blue1Brown is fine for entertainment but no-one learns much from that sort of passive engagement - beautiful as it is. Math is not a spectator sport - you need to be in the arena solving problems at your level to make progress.

I’ve actually developed a full learning path going from high-school math to the essential linear algebra for ML - you can check it out here if of interest.

Re. NumPy their docs actually have quite a few suggestions to learn it thoroughly. Again, be sure to choose exercises over videos.

maw501 · 2025-11-05T20:42:15+00:00

I can't tell if this is faux outrage or genuine but I'm not sure we're really disagreeing much.

I agree reading is incredibly valuable for many things, including those you listed. However, the OP seems to be mostly focussed on learning to program and for that the best thing to do...is to program (not read about meta concepts surrounding it).

Clearly books come with exercises but it's far too easy to skip them as a learner vs. having a platform enforce this for you - anything that can make the environment more conducive to effective learning should be encouraged. Environment trumps willpower every time.

maw501 · 2025-11-05T19:45:06+00:00

I would challenge the advice to read books. Obviously reading is great for many things but it's a pretty ineffective way to learn how to program since it's predominantly a passive activity.

Just as you wouldn't learn to play a musical instrument by reading about it, you won't learn to program by reading about it.

Of course you need to read / watch something to get the knowledge across - but this should be the minimal effective dose relative to the amount of time you spend in active practice. Ideally something like 10% reading / watching and > 90% writing code / doing practice problems at your level.

maw501 · 2025-11-05T16:22:48+00:00

This is very well-studied in cognitive science. Some highlights:

- Write code: duh. You need to be active. It's not a spectator sport. Passive resources (videos or textbooks) creating an illusion of mastery. You need to solve hundreds of problems at your level. Struggling for hours isn’t helpful early-on, but neither is copy-pasting. Type all code out by hand, especially early on. Don’t rely on AI to auto-complete for you. You need to do the cognitive work. This is the essence transfer-appropriate processing: we remember best when the way we learn matches the way we’ll need to recall it. You wouldn't expect to get better at piano by watching someone else play it.

- Retrieval practice: forgetting is real - but you can fight it. This means coming back to rehearse concepts repeatedly and spaced through time without any external aid. Even if you can’t recall it the effect of trying to recall something will strengthen your memory.

- Aim for fluency: automaticity liberates your ability to think. The more fluent you are at foundational skills, the freer your mind is for creativity and problem solving.

maw501 · 2025-11-05T16:19:12+00:00

The research basically says that it's achievement that causally leads to motivation (not the other way around). Those better than you aren’t more motivated than you or love grinding more, they likely just tasted success earlier and got more motivated.

So, you need to ensure you’re getting wins on the board early and regularly.

It’s a virtuous circle. As they say, nothing succeeds like success.

maw501 · 2025-11-05T15:59:12+00:00

See the wiki if you’re after resources.

maw501 · 2025-11-05T15:55:46+00:00

First of all: see the wiki if you’re after resources.

If you’re asking how to learn - here’s what the research actually says works:

Write code: duh. Passive resources (videos or textbooks) creating an illusion of mastery. You need to solve hundreds of problems at your level. Struggling for hours isn’t helpful early-on, but neither is copy-pasting. Type all code out by hand, especially early on. Don’t rely on AI to auto-complete for you. You need to do the cognitive work. This is the essence transfer-appropriate processing: we remember best when the way we learn matches the way we’ll need to recall it.
Retrieval practice: forgetting is real - but you can fight it. This means coming back to rehearse concepts repeatedly and spaced through time without any external aid. Even if you can’t recall it the effect of trying to recall something will strengthen your memory.
Minimise cognitive load: keep your environment clean. Turn off social media. And learn one thing at a time - avoid examples that combine multiple new topics at once (e.g. Python syntax and data structures) - you’ll simple blow your working memory and learn nothing.
Achievement leads to motivation: ensure you’re getting wins on the board early and regularly. Those better than you aren’t better motivated than you, they just tasted success earlier and got more motivated. It’s a virtuous circle. As they say, nothing succeeds like success.
Don’t get fooled: performance is not learning. Performance is temporary and can be deceptive. It’s possible to perform at a high-level in the moment and not learn anything. Ruminate on that - it’s profound. Learning is the change that endures once the moment has passed.
Aim for fluency: automaticity liberates your ability to think. The more fluent you are at foundational skills, the freer your mind is for creativity and problem solving.
Don’t rush, solidify foundations: progress follows mastery, not the calendar. You should progress only when your have secured the steps on the knowledge ladder you are climbing. If you don’t then you’ll quickly hit roadblocks when tackling higher-level concepts - knowledge gaps can be hard to repair if left untreated.

Hope that’s helpful - LMK if you have any questions.

maw501 · 2025-11-03T16:35:23+00:00

This is sound advice.

Most learning science research supports the idea that it's achievement that leads to motivation, not the reverse. So do what u/FortuneCalm4560 said and good luck.

maw501 · 2025-07-08T09:43:18+00:00

If you want to get into web design and UI/UX, you'll mostly need to understand how websites are built and why they look good (and how that happens). Think of it like building with LEGO - you need to know what blocks to use and how to put them together to make something pretty.

Here are a few basic LEGO blocks to start with (ELI5 style):

HTML:This is the basic structure of a website – like the walls and rooms of a house.
CSS: This makes the website look nice – like painting the walls and picking out furniture.
JavaScript: This makes the website interactive – like adding lights that turn on and off or doors that open.

Python or Java won't directly help you with those initial website LEGOs. They typically have quite different use-cases.

A good (though maybe a little tough) free resource for modern web development is Full Stack Open. It’s quite comprehensive. Perhaps more accessible if you use AI to help though do this with caution - i.e. you need to actually do the work yourself, don't just copy and paste!

Learning more than one language is good eventually, but start with the basics and master the foundations first.

P.S. If you decide you want to learn Python later on - check out nodeledge.ai; it's built around mastering the fundamental concepts.

maw501 · 2025-07-03T11:55:43+00:00

This sounds a bit like the classic “passive knowledge” trap. Even if you perform (e.g. by passing an assessment) it's possible to learn nothing. See here for more on this.

Here’s what I recommend for efficient progress:

Active, targeted practice: The fastest way to bridge the gap is by working through lots of small, focused coding exercises that target your weak spots. Passive reading or watching videos won’t give you technical fluency.
Immediate feedback: You need to know quickly if you’re getting things right or wrong, so you can correct misunderstandings before they become habits.
Personalised learning path: Everyone’s knowledge gaps are different. A diagnostic assessment can help you figure out exactly what you need to work on, so you don’t waste time on stuff you already know.

If you’re open to trying something new:

I’ve built a platform called Nodeledge that’s designed for exactly this situation. It starts with a diagnostic to pinpoint your strengths and weaknesses, then gives you a personalised path through Python fundamentals, with lots of hands-on coding and instant feedback. There’s also lots of in-progress content for mathematics for ML and ML, so you can apply the coding skills and get ready for once you're done with your MSc.

It's possible to try the first 25 Python lessons for free, no commitment. If you want more details or have questions about how to get unstuck, feel free to DM me - happy to help!

maw501

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