u/aspiring_aiengineer

I’m currently working as an AI Engineer, mostly on LLM-related work (fine-tuning, LangChain workflows, evaluation, FastAPI, and some cloud). Although I graduated with an ML background, I haven’t actively worked on classical ML or statistics for about a year.
I want to revisit ML and strengthen my statistics, especially the practical side. I’m not looking for beginner playlists or derivations. I’m looking for intermediate-level resources that focus on applying statistics to real datasets—hypothesis testing (t-tests, ANOVA/F-tests, etc.), assumptions, inference, forecasting, and choosing the right statistical methods in practice.

Any recommendations for YouTube playlists, courses, or books that are practical and application-oriented?

I’m currently working as an AI Engineer, mostly on LLM-related work (fine-tuning, LangChain workflows, evaluation, FastAPI, and some cloud). Although I graduated with an ML background, I haven’t actively worked on classical ML or statistics for about a year.
I want to revisit ML and strengthen my statistics, especially the practical side. I’m not looking for beginner playlists or derivations. I’m looking for intermediate-level resources that focus on applying statistics to real datasets—hypothesis testing (t-tests, ANOVA/F-tests, etc.), assumptions, inference, forecasting, and choosing the right statistical methods in practice.

Any recommendations for YouTube playlists, courses, or books that are practical and application-oriented?

I’m currently working as an AI Engineer, mostly on LLM-related work (fine-tuning, LangChain workflows, evaluation, FastAPI, and some cloud). Although I graduated with an ML background, I haven’t actively worked on classical ML or statistics for about a year.
I want to revisit ML and strengthen my statistics, especially the practical side. I’m not looking for beginner playlists or derivations. I’m looking for intermediate-level resources that focus on applying statistics to real datasets—hypothesis testing (t-tests, ANOVA/F-tests, etc.), assumptions, inference, forecasting, and choosing the right statistical methods in practice.

Any recommendations for YouTube playlists, courses, or books that are practical and application-oriented?

I’ve only fine-tuned models using Unsloth⁠ so far, but now I want to build a small language model (SLM) from scratch using PyTorch⁠ to understand the full training pipeline better.
I’m looking for guidance on:
• The step-by-step process for building an SLM from scratch
• Recommended model size for a beginner project
• GPU requirements (VRAM, number of GPUs, etc.)
• Approximate dataset size needed

Recently i found that use of multi vector retrieval will improve the retrieval quality of the RAG.But as we know storage will be 10x than normal compressed vector embedding, why no one are using colbert for retieval

Literally i havent seen anyone using this
I know cost will be more but some people care about the accuracy than the cost

Also if you are using colbert, will you create embedding for each tokens???
Educate me

Today I completed an interview. I answered most of the questions well, but I fumbled one basic deep learning question. I knew my answer was wrong, but instead of stopping, I kept adding unnecessary explanations, which made it worse. The interviewer noticed the mistake and corrected me on that point.

I’m an ai engineer over 1 years of experience.It’s been one year that i have done coding manually.Literally these days I’m relying on coding agents. I am planning for a switch.I’m good in theoretical understanding but in code i sucks.I can solve leetcode easy problems but struggles alot for harder ones. Which part i need to concentrate more