Of course my actual curriculum is a lot more in-depth.

Since ML topic start my progress has taken a hit, I am practically crawling but I am still happy not calling it quits and moving forward little by little everyday.

I spent a long time working through courses, nodding along, feeling productive, and retaining very little. The second I forced myself to build something small and broken on my own, even poorly, things started sticking. Passive watching gave me the illusion of learning. I am not saying courses are useless, they are good maps, but for me the learning only happened when I was confused and debugging my own mess. Did others hit this wall, or does the course only approach work for some people?

I went into this thinking: GPT-2 is only 124M parameters. How much memory could training it possibly need?

Turns out, quite a lot lol.

I traced the second training step of my from-scratch GPT-2 in PyTorch. Setup: one A40, fp32 AdamW, batch 8, 1,024 tokens per sequence.

Peak allocated memory: 17,054 MiB. Weights + Adam + masks and persistent overhead at that point: about 1,494 MiB. I was counting what the model owns and missing almost everything training makes.

The bit that surprised me most: four vocab-sized tensors take up 6,282 MiB at the peak. My block hooks only saw 13,913 MiB; the allocator trace caught the extra spike inside log-softmax backward.

I also tested batches in fresh processes. 20 worked, 21 didn't. At batch 20, peak allocated was 40,375 MiB, while peak reserved was 44,948 MiB. So just dividing the card's capacity by the tensor budget wasn't enough.

I put the whole thing into my new blog post, with the maths, a stacked memory chart, an interactive step-by-step scrubber, and a batch/context calculator. Code and raw CSV are there too. The calculator is an estimate, not a guarantee that a batch fits.

Post: https://angry-kratos.github.io/posts/2026-10-training-memory/ Code: https://github.com/angry-kratos/angry-kratos.github.io/tree/main/code/training-memory

I'm learning the maths behind ML systems by building and measuring them. If you spot something off in the accounting, tell me. That's the whole point of putting the experiments out there.

I started learning CUDA a while back to work on inference and understand FlashAttention. I read a lot of blogs on various topics, but the order was always confusing, so I wrote up the order that I'd pick if I started over.

What it covers:
- H100 architecture
- CUDA software mental model
- Getting started with Matrix Multiplication and building up to a very optimized solution
- Understanding how all of these add to optimization constraints
- An introduction to reading and understanding PTX(the compiler code)

It's meant to be a starter overview that will get you confident enough to read deeper, niche resources. I'd appreciate any feedback or corrections. Cheers!

Link to blog

First I felt that such large logic, implementation and intelligence just reduced to such small lines of code made learning such vast concepts meaning less since it's just reduced to like 3 lines of code

Later realised this is the beauty of learning ie learn how the backend works and implement it flawlessly through frontend

Am I the only one who feels this way though?

New learners often stall trying to get access to the biggest, newest model, as if that is what stands between them and understanding. In my experience the model choice barely matters for learning. A smaller, older, freely available model is more than good enough to teach you data handling, training loops, evaluation, and why your results are bad, which is where the real learning is. The bottleneck is never the model, it is your understanding of the problem and the data. Spend the energy you would waste chasing access on actually running experiments with whatever you have. Good enough and in your hands beats the latest and out of reach every time for a student.

Hi everyone. I’m a hands-on AI engineering leader with a PhD in computer science and experience teaching in academia and mentoring working professionals. My work involves both leading teams and building AI systems—from architecture and implementation to evaluation and deployment.

Based on that experience, I’ve been developing Nextia Learning, a free platform covering the different skills involved in AI engineering.

The motivation is straightforward: AI engineering requires knowledge across several areas, and knowing what to learn—and in what order—can be difficult.

Understanding models matters. So does knowing how to prepare data, build APIs, work with databases, evaluate outputs, manage permissions, deploy applications, and investigate failures. With LLMs and agents, you also need to think about retrieval, tool access, unpredictable outputs, latency, and cost.

You don’t need to master everything before you start. But understanding how these pieces fit together helps you build better systems and identify where you need to develop your skills.

That’s what I wanted to reflect in this platform.

Courses covering different aspects of AI engineering

The curriculum brings together:

• Foundations: Python, Git, SQL, mathematics, and statistics.
• Models and applications: machine learning, LLMs, RAG, agents, and MCP.
• Software engineering: APIs, databases, authentication, permissions, and frontend considerations.
• Quality and safety: evaluation, testing AI applications, security, and privacy.
• Deployment and operations: Docker, cloud deployment, CI/CD, observability, scaling, and cost.
• Leadership and governance: planning AI products, leading teams, and making decisions about risk and accountability.

The selection reflects the range of problems and decisions I’ve encountered while building AI applications. My aim is to help learners develop skills across these areas and understand how to use them together.

Learning paths are a central part of it

Having a collection of courses doesn’t necessarily tell you where to begin.

A software developer moving into LLM applications has different needs from a new graduate learning machine learning, a domain expert applying AI, or someone leading an AI team.

The learning paths organize courses around those goals, with prerequisites and a suggested order. They help you see what to learn next, how it builds on what you already know, and where it fits into the work you want to do.

Lessons are text-first, written in simple English, and include worked examples, guided practice, common mistakes, and checks for understanding. There’s also an interview preparation section.

A few things to be transparent about

• All courses and certificates are free. You can read lessons without an account. Signing in lets you enroll and save your progress.
• The content is AI-assisted and human-reviewed. I design the outlines and instructional approach, guide the examples, and review the courses. This is disclosed on the site.
• Your data isn’t sold or used for marketing. Lesson reminders are optional, off by default, and you control their frequency.
• The platform is evolving. Some courses are in pilot or marked coming soon. Feedback and corrections are welcome.

You can explore the courses and learning paths here: https://learning.nextia-ai.com/

For those learning or working in AI engineering: which skills were missing from your initial learning but turned out to be important in your work? I’d also welcome feedback on whether the learning paths make sense for your background and goals.

Advice I keep getting is to build intuition for gradients before drowning in formulas, but nobody says what the intuition actually is. Right now a gradient is just a thing I compute. For those who have the intuitive grasp, how do you picture it? Is it the slope direction, a compass pointing uphill, something else? I am trying to get past treating it as a symbol I manipulate and actually feel what it represents. Concrete pictures welcome, I learn way better from those than from the notation.

An exercise to help build the right mental model for Python data. - Solution - Explanation - More exercises

The “Solution” link uses 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 to visualize execution and reveals what’s actually happening.

Hi everyone,

I’m a self-taught software engineer with a couple of years of industry experience. I was recently made redundant, so I have a good chunk of free time on my hands before I dive heavily back into the job hunt (which, honestly, feels a bit daunting right now).

Lately, I’ve become really interested in Machine Learning and want to start learning it. However, I’ve been reading a lot of discouraging posts lately. It seems like even people with CS Bachelor's degrees are struggling to land MLE roles because they are competing against a massive pool of Masters and PhD graduates.

Given that I don't have an academic background or official credentials in ML, am I wasting my time trying to break into this field?

I’m absolutely willing to put in the work to learn, but I want to be realistic about my employment prospects. For those working in the industry: is there a viable path for an experienced, self-taught dev to pivot into ML/AI roles, or should I focus my energy elsewhere?

Thanks in advance for any insights or brutal honesty.

Which laptop should I buy as I am in my final year and in Aiml sector and i don't do gaming.

Should I go for m3 air or windows with rtx graphic card and budget around 1k $..

Thank you for your words ☺️

I've been working on DecisionFormer, an open-source project that lets you convert regular LLMs into decision models that predict choices directly instead of generating text.

Thought it might be useful for people experimenting with decision models, model evaluation, or alternative approaches to inference.

Would be interested to hear what people think or what improvements would make it more useful!

I'm in my 4th year of studying AI and Data Science, and I've been interested in astrophysics for a long time but didn't specialize in it, yet I still want to contribute to this field.

My question is, can I apply machine learning concepts to astrophysical problems in my current level? At my university we studied 2 years of preparatory courses in maths, calculus, physics, and some computer science and I only started delving into AI in my 3rd year, so my knowledge is limited in both AI and Astrophysics. I know the basics of AI/ML, namely CNNs, NLP, supervised and unsupervised learning...

I want to do interdisciplinary work with AI/ML in either astrophysics or medicine. I'm considering astrophysics for my Masters and plan to leave medicine for the future, when I'm a little more scientifically/academically mature, and perhaps make it a long-term career.

I understand astrophysics is not an easy discipline, but I will not be delving too deep into the theoretical concepts. I will mainly use AI/ML for things such as classification and read some literature on the current frontiers.

My question is: should I pursue this? Or is there something else that might interest me that I might not know about? How realistic is it for someone of my current level to learn valuable skills and expertise for such a project?

Hey everyone!

I've been learning Python and I'm really interested in getting into machine learning and deep learning. The problem is that whenever I try to figure out how to get started, I end up finding a huge amount of mathematics, statistics, linear algebra, calculus, and theory that I'm apparently supposed to learn first.

I understand that math is important if you want to properly understand how these models work, and I'm not trying to avoid learning it forever. However, I don't want to spend months studying theory before I can actually start building things and experimenting with code.

I'd much rather learn by doing projects, making mistakes, seeing what happens when I change something, and gradually learning the theory behind it as I need it.

Is it realistic to start building ML projects with Python while learning the math along the way?

How much math do I actually need to know before getting started with things like neural networks and deep learning?

Would you recommend starting with scikit-learn and traditional ML, or jumping straight into PyTorch or TensorFlow?

Are there any resources that teach you by making you build actual projects instead of spending most of the time on theory?

I'm not looking for a way to completely skip the fundamentals. I just want to avoid getting stuck in tutorial hell or spending so much time preparing to learn that I never actually get to build anything.

I'd really appreciate any advice, especially from people who learned this way themselves. Thanks!

As part of my RL journey, I just released 1-page RL notes on Q learning!

Kept it

• short, coherent with
• code snippets and
• required math with explanations

Link to the website and let me know your thoughts

Cheers!

Title: How do you introduce yourself in an AI Engineer interview?

Hey everyone,

I feel like my self-introduction isn't that good during AI Engineer interviews.

I have around 3 years of IT experience, but I sometimes struggle to explain my experience properly.

If you're working as an AI Engineer, GenAI Engineer, or LLM Engineer, could you share how you introduce yourself in interviews?

Even a simple example would really help me improve mine.

Thanks in advance!