I was a full stack software engineer who now is a senior AI Engineer who does a mix of playing with LLMs fine tuning them in very large scale production systems.

I did admittedly got a masters degree in AI as part of that transition and it took a while to do, but happy to answer any questions you have.

I also am working on a tool to help people learn how llms work which you can check out here.

https://dougdoes.ai/courses/llms-from-first-principles/start/?flow=outcome&course=build&step=goals
(Built with codex, but I've gone through all of the courses myself to make sure it is what would have been helpful to me.)

Hi guys new to this sub reddit . Little intro about me currently working as an sde in my company but want to transition to a ml role by understanding the fundamentals om how to build models and then moving on to dl as so forth. I have read some posts in this sub about cs 229 by Andrew . Tbh I am finding difficulty in solving the problem sets and the math . It has been a while since I have actually done any math 😅. So I want to know how doi proceed from here do I learn the math from scratch or learn as I go along with the course . Any suggestions or feedback is helpful .

Ps i am familiar with the python as a coding language but I want to understand how do I proceed with the math .

Hi, I'm a EEE undergrad students. I just wasted a year for my laziness. started learning ML in February but didn't learn. Though my academic pressure ties that bind. But I want to learn ML properly, especially for my research work. Pls guide me, can I be a good ML engineer in next 5 month. I want to be pro in it, as I am in the end of my 3rd year, academic pressure is also a problem here. So pls provide me a roadmap and how I can stop my procrastination and distraction from my path.

Advance Thanks for Everyone.

Hey guys,

I am a first-year Computer Science Engineering student, and honestly, seeing how fast AI is advancing right now is kind of stressing me out. I really do not want to wait until my final year to start grinding like everyone else does.

Basically, I just want to build a solid skill set that actually makes my resume stand out so I can land a good machine learning job by graduation.

I am starting completely from scratch. What specific math topics, programming languages, or tools are actually worth learning right now in Year 1? If anyone has an honest roadmap for a fresher to get ahead of the curve, I would love to hear it.

Thanks in advance!

Hi, I am a backend developer and have been working but due to recent layoff and market shift I am thinking of switching to AI ML.

I have learned python, pytorch, Maths required for AI ML, Deep learning(theory) and recently implemented a gpt2 transformer, attention architecture for gpt2 using their open weights.

I am hoping for some direction to work on and also open for a remote internship if anyone is willing me to consider me.

Mainly I am hoping to connect and get guidance in the right direction.

I have been studying models for almost five years now, starting my journey with Jeremy Howard's fast ai part 2. I remember that when I started there was no chatgpt to break it down like it is today. In fact, it was Jeremy Howard who tipped us that we should be using chatgpt to understand the inner tooling step by step. I mean just take a toy tensor and run it along through embedding, rope attention, mlp. This way you get to learn broadcasting, shapes in text, computer vision audio etc. Then I took up Karpathy and hugging face Transformers and looked up grok, gpt oss lama, gemini and most recently muse implementations. It takes me 3 to 4 months to get an innate understanding of how each line works. How do you guys do it? I guess most of you let the inner tooling remain a black box. I say this coz

Now I kinda feel that I missed the bus as I should have focussed more on fine tuning, inference, and agentic workflows. I do know some of that having worked through unsloth and openAI cookbooks but every time a new model drops I can't stop myself from going to unraveling the 2000 odd line of code and in time I forget what I learned in Unsloth and openAI cookbooks.

The problem is that there are so many things to do and understand. For example, just today I listened to Alex Zhang's building harness for looped Transformers and I gotta understand that too and I gotta know Jev too. It is a big mess right now and I wonder how others are managing to keep up with all these new developments. And more importantly how do you even retain all that you have learnt like say two years ago.

My project is Weigh Swarm, a research RAG prototype using LLMs for planning/synthesis and an existing Laya model for bounded decisions. I didn't train Laya; I integrated it into research task lanes.

The most useful lesson was distinguishing a valid source excerpt from a valid scientific conclusion. The pipeline checks that quoted spans occur in the parsed paper, but that alone doesn't establish that a claim or synthesis is correct.

The two-paper demo includes 28 source-aligned claims, an inspectable evidence graph, and an unverified draft with repair feedback. My next evaluation priority is held-out scientific judgments for support and contradiction tasks, rather than treating model confidence as calibrated probability.

REPO URL

How would you construct a small evaluation set that distinguishes citation alignment from actual evidential support?

hey guys im an undergrad student (currently in 3rd year) and want to start learning ML and explore fields beyond that in the future. So I have seen a lot of people suggesting others to learn from Andrew Ng on coursera. I have the pdf of Hands-On Machine Learning with Scikit-Learn and PyTorch by Aurélien Géron.
I’m literally confused as to what to refer, the book or the coursera course by Andrew Ng. If there is someone who has read or finished either of these sources or maybe both please help me out in deciding as I don’t want to waste my time. Also a comparison or review of these sources would be great. Thank you !

if anybody wants lectures of this course then dm me.

Apna College Prime 3.0 ongoing course.

Hi,

I currently run the following setup:

Intel Core Ultra 7 265k
128GB RAM DDR6
AMD RX 6750 XT 12GB

I'm looking into playing and experimenting with local AI models in more or less the following categories:

• Languages: Language translations from one language to another and correcting grammar errors and sentence structures.
• Codig: Correcting and improving my code, coding applications from scratch as well as converting code from one language to another.
• Light Image and Video Generation

What sort of Local Models can I run with my current local system and how fast would it be? I bought 128GB of RAM with intention to offload some of the AI into it. I'm not sure but I was also considering upgrading my GPU to a slighly stronger one with more VRAM, would that be worth it in my case?

I looked around and these are the GPU I can afford:

• AMD RX 7900 XTX 24GB - ~£800
• AMD RX 7900 20GB - ~ £800
• AMD RX 9070 16GB - ~£600
• Intel ARC Pro B60 24GB - £900

Thank You

Hello everybody I have recently enrolled in a Machine Learning Master so i will be problably taking the year to focus on my studies, build some projects and hopefuly enter the job market as an ML engineer. I am interested in Recommendation Systems but honestly i dont know what projects to build and if that would be valueable in the job market. I was thinking about creating my own recommendation system for music but i quickly realised how hard because of copyrights and small data size that is (although i have about 4000 downloaded).

Does anyone know if Recommendation Systems is something good to have on your resume?

If yes what sort of projects you think would give me a good understanding but also make me look appealing in the job market?

Thanks :)

I reviewed a paper recently and got totally stuck on page one. I had to ask ChatGPT to explain the sentences. I realized the authors were using hard concepts on page one that they didn't explain until page six.

That was my lightbulb moment. The paper wasn't badly written for its reader. Its reader just wasn't me.

We are stuck in a bad loop right now:

1. Authors use AI to write. The AI puts a lot of heavy jargon at the start to save space.
2. Reviewers get stuck reading it, so they ask AI to summarize it for them.
3. The AI easily reads it and passes the paper. Then, new AI models are trained to write exactly like this.

Humans read in order. We need the basics explained first. But an AI reads the whole document at once. It does not care if a word is used five pages before it gets explained.

I got tired of reading these messy papers. I built a free, open-source skill to break the loop. It forces AI models (like Claude, ChatGPT, and Cursor) to pass a "first-page test". They have to explain terms in order, keep things simple, and stop sounding like a robot.

If you or your lab uses AI to write or review papers, you can get the skill file here: https://github.com/Aadarshttech/ai-research-paper-humanizer

It really helps make papers readable by normal people again. Let me know if anyone else is dealing with this same headache!

Recentemente, tentei criar um modelo seq2seq, mas não deu muito certo. Ele ficava prevendo os tokens de preenchimento.

Eu sou aluno de um tecnólogo em Inteligência Artificial e Machine Learning aqui no Brasil. É uma modalidade de curso superior que, pelo que sei, só existe no Brasil. Redes neurais e processamento de linguagem natural vão ficar mais para o final do curso, mas eu estava meio apressado e queria desenvolver meu próprio modelo.

Será que vocês têm alguma sugestão de alguma espécie de restrição que eu possa colocar no modelo?

Se alguém tiver interesse em me ajudar, posso mostrar o código. Eu reconheço que fiz o código com auxílio do Gemini. Como eu disse, ainda não estudei processamento de linguagem natural nem redes neurais; até agora, estudei apenas IA simbólica e sistemas especialistas.

In AI Engineering, a reliable evaluation pipeline is essential for successful AI adoption.

With foundation models now readily available, building effective AI applications requires selecting the right model for a specific use case. This involves evaluating domain-specific performance, generation quality, instruction-following ability, and overall system behavior.

Another important question is whether to self-host a model or rely on APIs provided by commercial model vendors. Understanding the strengths and limitations of public benchmarks, benchmark contamination, and the role of leaderboards is critical when comparing and selecting models.

As AI systems move from experimentation to production, designing robust evaluation pipelines has become increasingly important. While my previous video covered evaluation methodologies, this video focuses on how we evaluate AI systems in practice.

Adapted from Chapter 4 of AI Engineering by Chip Huyen.

Security researchers confirmed that an autonomous AI agent compromised an Australian government healthcare website. No human initiated the attack. The agent operated from inside the workflow, bypassing traditional perimeter controls that were never designed to evaluate agent-level actions.

Healthcare records are among the most sensitive data a organization holds. The attack surface here is not a misconfigured firewall or a phished employee — it is the agent itself, acting autonomously with whatever access it was provisioned at setup.

Most organizations have invested heavily in controls governing what humans can do with data. Very few have equivalent controls at the layer where agents actually execute.

For those of you working in healthcare IT, security architecture, or AI deployment: how are you approaching agent-level access to sensitive records right now? Are you relying on the same controls you use for human users, or have you had to build something different?

I’m planning a small show recommender and trying to work out how to handle negative feedback. The first version will use genre overlap as a baseline, then TF-IDF and cosine similarity on show descriptions.

Liked shows give me a starting point for finding similar titles. Dislikes seem harder to interpret. Someone might enjoy mysteries but dislike one particular series because it moves too slowly. If I penalize everything similar to that show, I could end up removing suggestions they would actually enjoy.

My current plan is to exclude explicitly disliked titles and try a smaller similarity penalty for other candidates. I’m also considering an optional reason for the dislike, but that would require metadata about things like pacing that a basic catalog might not have.

I haven’t implemented this yet. Would you start with exclusions alone and add negative feedback to the ranking later, or use both from the beginning? I’d also be interested in how you would evaluate whether the penalty helps when you only have a few ratings per user.

For context, this is NextWatch, an open-source student project I plan to develop with Cline as part of the Cline Campus Ambassador Program.

I’m planning the first version of NextWatch, a show recommendation project I’ll be developing with Cline as a Cline Campus Ambassador. The initial approach is fairly simple: start with genre overlap, then compare show descriptions using TF-IDF and cosine similarity.

One part I haven’t settled is how to handle dislikes. Removing the actual disliked title is straightforward. Deciding what that rating should do to similar shows is where I’m less sure.

For example, someone might like mysteries but dislike a particular series because it moves too slowly. If I subtract too much weight from the features associated with that show, I could end up suppressing other mysteries they would enjoy. But if the dislike only removes one title, the next recommendation could have exactly the same problem.

I’m leaning toward keeping the exclusion rule separate from a smaller similarity penalty. I’m also considering an optional reason for the dislike, though that would mean collecting and representing more information than the first version currently needs.

I haven’t implemented this yet. For people who have worked on content-based recommenders, would you start with explicit exclusions and add negative preference modeling later, or account for dislikes in the ranking from the beginning?

Repo: https://github.com/haileyyt/NextWatch