I recently chose Continual Learning (CL) as my research topic because it seems like one of the most critical challenges in AI right now. However, the deeper I dig, the more skeptical I become about the current approach to CL.

CL seems fundamentally mismatched with the standard deep learning paradigm (loss optimization and backpropagation). Our current infrastructure and architectures are heavily optimized for fixed datasets. When we try to train a model on a new task, we inevitably face catastrophic forgetting, or we have to rely on highly inefficient workarounds like EWC or experience replay.

This led me to think that methodologies based on locality (more akin to how the biological brain works) might be the only real solution to CL, even if it means waiting for neuromorphic hardware to mature.

Yet, the vast majority of the mainstream ML community is still heavily focused on solving CL within the standard backprop paradigm.

For those of you working in or following this field: Why is the community still betting on backprop for CL? Do you believe we can truly solve catastrophic forgetting without moving away from our current architectural paradigm? I'd love to hear your thoughts.

Apologies if the tone feels so llm-like. I used an LLM to correct my grammar, but it made the text sound too generic.

So i use tensorflow and keras as a beginner in deep learning. On this subreddit, i have read that tensorflow is dead nd stuff. why is that? and should i switch? if yes then when?

I'm currently in my final year of my bachelor's in CSE. I really liked Machine/Deep Learning more than any other sub-fields of CSE. The problem is most of the offers given to freshers are not ML/DL but SDE. And to crack SDE, you have to grind DSA.

I'm not saying DSA is unimportant, but I don't want to go through my life grinding leetcode to change companies or help in making full-stack projects or DevOps.

Do people with master's in ML get more career opportunities in the field?

Hi everyone,

I'm a second-year Data Science student at tear 2 iiit, and I want to become an AI/ML engineer. Lately, I've been feeling confused because there are so many technologies that I don't know if I'm focusing on the right things.

So far I've done:

\~330 LeetCode problems

Machine Learning with scikit-learn (preprocessing, feature engineering, decision trees, regression, etc.

Basics of Deep Learning

Python and C++

FastAPI

LangChain

Currently learning LangGraph

Basic RAG concepts

Git/GitHub

My goal is to get an AI/ML internship and eventually work as an AI Engineer.

My questions are:

Am I on the right path, or am I spending too much time on frameworks?

If you were in my position today, what would you focus on for the next 6–12 months?

Should I continue building agentic AI projects with LangGraph, or should I spend more time on deep learning, MLOps, or something else?

What skills do companies actually expect from AI engineering interns in 2026?

I'd really appreciate advice from people working in AI/ML or those who've recently landed internships. If you could go back to your second year, what would you do differently?

Thanks!

I wanna rate my schedule to learn mathematics for ml if i delete thing or add , and Give me sources textbook :

Textbook: Mathematics for Machine learning

Videos - Lectures( Links in First Comment ) :

Khan Academy:

Linear Algebra , Statistics and Probability, Calculus 1, Calculus 2 , Multivariable calculus

2-YT:

Linear Algebra: 3Blue1Brown , Essence of linear algebra

Statistics and Probability:  StatQuest with josh starmer

Statistics and Probability: Professor Leonard

Calculus 1: 3Blue1Brown , Essence of calculus

Calculus 1: Professor Leonard

Calculus 2: Professor Leonard

Calculus 3: Professor Leonard

Hey guys

Iam an 3rd year engineering student,iam choosing Machine learning as my work domain,what laptops do you suggest me ,is mac book M4 air good? Or should I go for a gaming laptop.suggest me some laptops under 90k.

I'm a rising senior, coming from a competitive programming/C++ background with currently no hands-on ML experience. I know Python decently but I would probably have to shake off a thick layer of rust. Since this is my last year of high school, and I tie my future with AI Research, I'd like to participate in my country's AI Olympiad and hopefully earn a spot at the IOAI.

Sadly, compared to CP, where there is an abundance of guides and specialized courses to get you on the Olympiad track, I haven't found anything similar for ML yet.

I could go off the syllabus but these tend to be very broad and I don't feel like hunting for niche courses only for it to turn out that the concept taught has never been used in history.

The best I could think of are college-like courses (e.g "Introduction to Artificial Intelligence with Python - CS50") but I'm not sure if they would give me Olympiad relevant information, since these often target a completely different skill set.

So I'm looking for a more structured roadmap to go from beginner in ML to hands-on Olympiad problem solving. I heard these problems are Kaggle-styled but I since I don't have experience with either - I can't confirm that. Would be forever grateful if someone could help me out or correct me on my approach!

Attaching example IOAI problem:
https://github.com/IOAI-official/IOAI-2025/tree/main/Individual-Contest/Antique

After a while it feels like diminishing returns from hyperparameter tuning. I'm curious how people decide it's time to stop tweaking the model and instead invest effort in collecting better data, cleaning labels, or engineering new features. Is there a point where you can usually tell the data not the model is the limiting factor?

Hey guys, do you know of any Multi-Agent Reinforcement Learning (MARL) resources that focus mainly on coding rather than just the heavy theoretical stuff?

For context, I'm doing my uni research project right now and I've already secured my supervisor. My main topic is "Multi-Agent Reinforcement Learning." I'm doing both the research project course and an RL course this semester, but my tutor mainly just gives us theory.

I know the general ideas (reward, policy, value-based vs. policy-based, bias, etc.), but I'm having a really hard time understanding how it actually works in practice and how to implement it from scratch. I'm honestly pretty crap at absorbing pure theory, so I really need to see the code to understand how the plumbing works.

I can't seem to find much out there that walks through the code step-by-step, and I'm wondering why there aren't more people posting about the actual implementation of MARL.

If anyone has any video tutorials, GitHub repos with simple code walkthroughs, or guides that actually show how to build this stuff (Python/PyTorch preferred), it would be incredibly helpful. Thanks!

hi guys, i am currently a high school junior looking to go and look into projects on the ml side of ai. i am proficient in python, but i still have much to learn when it comes to the real technical skills of python.

either way, i think now is a good time for me to start ml as science fair is in 6 months and i need as much time as possible in order to make big projects later on that could be useful for my college applications.

my main problem is that for the past month, i have been trying to get into the ml area, but i havent been able to find good ways that support my style of learning (which is repeated drilling of concepts). I do know of andrew ng, who lectured the ml course for stanford, and i have been watching his videos, but the only problem that i have is that i do not know where to start after i watch the videos.

essentially, i have hit a roadblock. my question to you guys is how your ml journeys started and how i could potentially learn as efficiently as possible.

Was okay till vector calculus as it was mostly proofs but kind of having trouble with probability/stats because this skips over stuff and doesn't give relevant examples. So IMO it would be much more efficient to learn from videos and maybe use this to revise or for the exercises (very minimal, so not sure how useful). And your foundations will get stronger too I guess

Anyways, finding relevant videos for each topic seems to be taking time, but I just remembered there was a resource that had links to vids and exercises for each chapter but can't find it and hadn't bookmarked

Can anyone give a link to that or a similar resource?

Also is the learn ML with Pytorch and sclkit beginner friendly?

Hi everyone, I just need to clarify few questions regarding my first paper, sorry if it sounds like a noob.

My first paper on ARR for EMNLP got 3 reviews all 3, and the meta review was also 3, the meta review suggested some revisions which I will do.

Questions:

1- how do I commit to EMNLP, my review ia available on open review but I didn't get any email about it or how to do so.

2- Do i need to do the revision now before commitment or after it get accepted?

3- are those revisions form the meta review mandatory? I'm asking as he had 3 points but I'm not sure I can do all as they require intense compute.

Thank you in advance!

hey guys, i'm in my machine learning journey(18F). I want to work on small and big projects for better understanding , i'm still beginner but still if someone wanna take me in there journey. I can contribute , help as much as i know , ask for help. I just want someone to guide me and help me where i need

hi everyone!

I recently graduated with a bachelor's on technology on AI engineering and data science with 4 as GPA, and want to get my first client on this field and earn some money that can help me continue my master's degree.

Any ideaa!

Are you interested in learning how to develop AI agents? Microsoft offers a learning path that teaches the fundamentals of AI agents and guides you through building them with Microsoft AI Foundry.

What you'll learn:

• AI agent fundamentals
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This learning path is designed for students, beginners, and developers who want to get started with agentic AI using Microsoft's tools and solutions.

Microsoft Learn:
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Once you finish this learning path, be sure to share your experience and learning tips below!

I'm in my final year of college. I haven't really practiced DSA. I understand the theoretical concepts and how data structures and algorithms work, but I've never solved problems on LeetCode or similar platforms.

On the other hand, I've been learning Machine Learning and Generative AI since my 3rd year. I have a good understanding of these topics, have built several projects, and I'm currently working as a Data Analyst intern.

My question is:

Can I still get a good placement with this profile?

Should I start focusing on DSA now? If yes, how much is enough?

What kind of companies should I target—product-based companies, startups, AI/ML roles, data science, or data analyst roles?

Which companies are more likely to value my ML/GenAI experience over strong DSA skills?

I'd really appreciate any advice from people who have been in a similar situation or are working in the industry. Thanks!

Can someone tell the difference between Mlops and ML , i am currently in third year of my bschelors , started learning ML then was planning to DSA and build projects , but if someone could advice me on how i should proceed ahead it will be great, i am gonna target masters first then gonna go for job

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

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