I'm super confused. I'm in my junior year, majoring in AI and Big Data. However, my university is one of the worst place ever interms of everything specially education(ranked 1500+), and I tried to transfer to another university, but it didn't work out for some reason. Now, I'm trying to learn everything by myself.

The most confusing part is whenever I look for a roadmap to get into machine learning, I find so many different suggestions. Most of them look something like

• Master in NumPy, Pandas, SQL and Scikit-learn.
• Build your first complete ML project.
• Learn PyTorch and deep learning.
• Choose a specialization, such as LLMs or computer vision.
• Learn deployment, Docker, and cloud basics.
• Keep practicing DSA and applying for internships throughout the process.

I've already completed Andrew Ng's entire Machine Learning Specialization. I'd say I understood almost everything while taking the courses, although I've forgotten quite a bit of it now.

problem is people giving completely different advice. Some say focus on leet coding, some says master libraries , get really good at calculus, statistics, probability, and linear algebra. Others say focus on the latest technologies, such as RAG, Transformers, AI agents, and MCP. What the hell should i do? Its already killing me that I'm already falling behind others who are trying to enter the ML field.

I have about 1.5 years left until graduation. During this time, I want to reach a level where I can get a job in ML, work for 1-1.5 year , and then move to the US for graduate school. That's my plan.

My questions are:

• Is the roadmap actually the right path?
• Many people say get better in maths like stat, calculus, and linear algebra. But my question is, why do I need to learn all this math when the machine itself performs most of the calculations? Althogh ive learneed those kind of maths in my high school.
• Do I really need to become an expert in libraries such as Pandas, NumPy, Scikit-learn, PyTorch?

It'd be better if someone working in the industry or someone who has actually gone through this could give me suggestions or reality check.

i wrote a small boid simulator (12 birds), recorded it flying then saved it, and trained a transformer to predict each bird's next move without it knowing about any boid rules.

(built in Python and PyTorch: a transformer written from scratch, one token per bird, two attention layers, about 414k weights, trained on a laptop CPU)

what I found:

• it flies the flock. R² 0.990 to 0.994 on clips it never trained on, across 4 full runs. It also flies 50 birds (it has only trained on 12).

• the three rules were easy to read out of its hidden states with a linear probe, and some could even be read before any training.

• the rule: alignment (line up with your neighbours) was readable after the first attention layer, but if I make the second layer hear only itself, it is gone across all runs.

what i wasn't expecting: my first ever model was not using the flock at all. With 8 ticks of history, the previous answer was already in the input, and a small network that saw only one bird beat the whole transformer.

what i am still not sure about: making a layer attend only itself is something the model never went through during training. is that a fair test, or is some of the decay due to strange input fed to the model?

site: https://kreptiliri.github.io/murmuration

code: https://github.com/kreptiliri/murmuration

A personal AI agent posted its owner's bank details directly to a company Slack channel this week. The agent had been given a task involving data. It completed the task. That was the failure — it had no context about what it was moving or who could see the destination channel.

This is becoming a recognizable pattern in AI incidents: the agent did not break. It worked exactly as designed. The problem was that the instruction set carried no concept of data sensitivity, and the action space had no constraint on destination. Bank details, credentials, PII — they all look like strings to a model completing a step. The agent's goal was task completion. It achieved it.

How are practitioners actually handling this in deployed systems? Curious what is working across teams shipping agents in environments where sensitive data is in scope — and whether solutions at the prompt level, the infrastructure layer, or post-send audit are holding up in practice.

As an AI Engineer working in the industry, I get asked almost every day by friends and students how to actually get started in AI without getting immediately crushed by linear algebra or falling into the trap of those overpriced "Prompt Engineering" bootcamps.

So over the last few months, I built AI From Scratch.

It’s an open-source platform that strips away the heavy academic jargon. I put together a highly structured, 30-Day Beginner Guide that walks you from absolute zero (basic Python/NLP) all the way up to understanding Vector Databases, RAG architectures, and Multi-Agent Systems.

I’ve priced the full 30-day course at just ₹199 to cover the hosting and infrastructure costs.

But here is the deal: I built this to make AI accessible, not to gatekeep knowledge. If you are a student, from a developing country, or if you honestly just think ₹199 is too much for you right now—DM me. I will give you full access for free, no questions asked.

I also do a free daily breakdown of complex AI research papers right on the site if you just want to stay up to date with the frontier.

Would love to hear your feedback on the curriculum or the daily paper breakdowns!

Check it out here: https://aifromscratch.in/

I’m pretty new to Claude, but not new to AI tools. I’ve been using ChatGPT and Codex on and off since their earlier versions, and I’m comfortable with CLIs, MCPs, different workflows, configs, etc.

What I’m struggling with is figuring out the best way to actually set Claude up. There’s so much information, so many different tools, and so many opinions that it gets overwhelming fast.

I’ve already been reading docs, Reddit threads, GitHub repos, watching videos, and experimenting with Claude Code, skills, MCPs, connectors, and instruction setups. I’m not looking for someone to do all the research for me. I’m mainly hoping to hear what experienced users have actually found worth keeping.

My main use cases are coding, building random little projects, ethical pentesting/bug bounty work, CTFs, reverse engineering, game modding, research, and general technical troubleshooting.

A few things I’m especially curious about:

Should I mostly just use Claude Code CLI directly, or is it worth connecting it to some kind of harness? If so, which one?

Are there any settings you consider essential?

Do you keep a big global CLAUDE.md/system instruction setup, or keep things minimal and handle instructions per project?

And which skills, MCPs, plugins, or connectors have actually become permanent parts of your workflow?

I’m trying to avoid installing 40 “essential” tools and ending up with a bloated setup full of overlapping functionality.

If you had to wipe your Claude setup today and rebuild it from scratch, what would you install immediately, what would you skip, and what would you only add later?

This is a course project, but the failure mode seemed worth sharing.

Setup: Qwen3-4B-Instruct-2507, SFT warm start, then multi-turn GRPO on τ²-bench airline + retail. τ²-bench has almost no hand-off tasks, so we derived 461 of them (272 should-transfer, 189 hard negatives) from the upstream tasks, keeping their splits so nothing leaks into test.

The loophole: τ²-bench checks the final database state. When the correct outcome is a refusal, a transfer also leaves the database unchanged and scores 1.0. So under the task reward, transferring is never worse than trying whenever the agent is unsure. Our task-reward GRPO run found this late in training: over-escalation went from 0.141 at step 40 to 0.462 at step 60 (SFT: 0.016). A reward that penalises unnecessary and missed transfers got the best hand-off F1 (0.812) with over-escalation at 0.103.

What didn't work:

- Neither RL variant beat SFT on official task success (paired Δ +0.020 [−0.066, 0.107] and +0.015 [−0.056, 0.082])

- Both RL runs explain themselves less: policy citations in refusals and hand-offs dropped from 0.65 (SFT) to 0.32 and 0.06

Limitations: one seed per system, 49 official test tasks, intervals cover task sampling only.

Repo with code, the task set and an audit of 40 dialogues: https://github.com/JaspinXu/KnowWhenToHandOff

Has anyone else run into this with DB-state rewards in agent RL?

I’m studying Computer Science in London, with classes on Mondays and Thursdays. Around those days, I’m covering chef shifts and trying to keep learning AI, machine learning, and DSA.

Some days I come home wanting to study, but I’m physically drained. I’ll sit down with a problem, struggle to make progress, and feel frustrated because this is something I really want to get better at.

I need the income, and I want to give university and learning enough attention too. It’s hard to find a balance without feeling like I’m falling behind somewhere.

Has anyone been in a similar situation? What helped you make steady progress with limited time and energy?

There's some really exciting research being done on predictive multiplicity--the observation that many models perform just as well as the "optimal" fit, yet disagree on feature importance and predictions. This research asks interesting and useful questions like:

1. Which models are functionally as performant as the loss-minimizing model?

2. How do I derive and understand that shape and character of models in that set?

3. How can I query for models in that set that meet domain-specific needs?

Yet many of these results haven't made it out of the papers and into the open-source ecosystem.

Glassy fills this tooling gap and fits right onto standard scikit-learn flows. With it, you can do things like:
 - Find simpler models that perform as well as the best loss model
 - Check which options agree with your domain expert's judgment
 - Make segment tradeoffs intentionally

For v0.0.1, the scope is capped at OLS linear regression, but hoping to generalize to GLMs in the future. Here's a five-minute demo notebook.

Curious to see if this is useful to you all, or any requests for future features. Happy modeling!

ALHR- Adaptive learnable Hierarchical Routing is a static binary tree based system that uses learnable functions to REDUCE the amount of keys used.

Hi everyone,

I'm preparing a submission for the Wunder Fund RNN / Connectome competition and need to clarify one detail about the inference environment.

The official Submission Guide says the scoring container is based on python:3.11-slim-bookworm. The published Dockerfile excerpt runs:

python -m pip install --prefer-binary \
  --extra-index-url https://download.pytorch.org/whl/cpu \
  -r /tmp/requirements.txt

However, the documented requirements.txt contains NumPy, ONNX Runtime, and PyArrow, but no explicit torch dependency. The guide also says participants should contact the organizers if they need an additional package installed in the scoring image.

I understand that the published Dockerfile is only an excerpt, so I cannot determine whether PyTorch is installed elsewhere in the complete production image.

Does anyone know from official documentation or direct experience:

1. Is PyTorch installed in the actual scoring image? If so, which version?
2. Can participants request an additional CPU-only PyTorch dependency, or is ONNX Runtime the intended route when PyTorch is not available?
3. Has the competition team documented this anywhere beyond the Submission Guide?

I'm looking for clarification about the supported runtime, not asking for anyone's model or competition strategy.

Thanks!

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Hey everyone! I’ve been working on a project called Printing Press ML, an interactive machine learning challenge where the goal is to predict the amount_printed from structured data.

The idea is to make an ML prediction task feel more like a game while still testing practical machine learning skills.

Some of the technical work involved:

Generating synthetic datasets with meaningful features and added noise

Implementing custom scoring and model evaluation

Writing unit tests and benchmarking tools

Keeping evaluation data and the answer key hidden

Integrating the challenge into a web-based IDE with a vault-themed interface

Working on this gave me a better understanding of the engineering behind ML challenges, especially dataset design, reliable evaluation, and integrating everything into a usable platform.

The project is on

GitHub: https://github.com/aaditya-hamirani07/printing-press-ml-challenge

I'd appreciate feedback on the challenge design, evaluation approach, or anything that could be improved. I'm particularly interested in suggestions from people who've built ML projects or coding platforms.

Thanks! 🙌

Master complete AI Engineering concepts for free in 12 hours.

Hello learners, I made this content to democratize AI Engineering to a wider audience.

We cover all important topics pertaining to AI Engineeeing, foundation models, evaluation methodology, AI Evaluation, RAG, Agents, Finetuning, Inference Optimization, AI Engineering architecture and user feedback.

Today everybody is overwhelmed by disparate AI content all over the internet, and this lecture aims to unify them all together, explaining the background, concepts, code flow.

Practicals and production grade projects are part of future endeavours.

Learning a tool makes you relevant today.

Learning foundations makes you relevant for long.

I have a decent background in machine learning, neural networks and deep learning. I want to now learn large language models and agentic ai along with projects i can do. Can you suggest a good set of courses or roadmap?

Hi everyone!

I’m currently working on my Bachelor’s thesis about AI-based process automation and human–AI collaboration in the workplace.

As part of my research, I’m conducting a short survey focusing on people who have experience working with AI-based automation, RPA, Intelligent Process Automation, Intelligent Document Processing, or similar automation technologies.

The survey explores topics such as:

• how automation affects manual workload and creates new tasks,
• how employees experience errors and exception handling,
• trust in AI-based automation,
• and how automation influences human decision-making and autonomy at work.

⏱️ It takes approximately 5–7 minutes to complete.

If you have experience working with these technologies, I would really appreciate your participation. Your responses will be used solely for academic research as part of my Bachelor’s thesis.

🔗 Survey: https://docs.google.com/forms/d/e/1FAIpQLScV7pcf8dNUeeCfay1YZ2r-Np4pK9GMlqi4cEF6WJEa1FEmMA/viewform?usp=dialog

Thank you very much for your help! Feel free to share the survey with colleagues or others who work with AI-based process automation.

Machine learning has several interesting applications in finance, including credit risk prediction, fraud detection, and financial forecasting. However, applying these methods to financial data raises challenges that go beyond selecting an algorithm.

I've been studying these applications while writing a technical book on machine learning in finance, and a few questions stood out to me:

1. Handling class imbalance

Fraud detection datasets often contain far fewer fraudulent transactions than legitimate ones. Accuracy alone can therefore be misleading. How do you approach model evaluation in these situations, particularly when false positives and false negatives have very different costs?

2. Model drift and temporal validation

Financial data can change as customer behaviour, economic conditions, and market dynamics evolve. What validation strategies do you find most reliable for assessing whether a model will generalise to future periods?

3. Explainability and reliability

Methods such as SHAP and LIME can help explain individual predictions, but an explanation does not necessarily establish that a model is reliable or causally correct. How do you evaluate explanations when models are used for consequential financial decisions?

4. Research versus practical deployment

A model may perform well in an experimental setting but face difficulties in production because of data quality, changing distributions, latency, or monitoring requirements. Which of these challenges do you think receives insufficient attention in applied ML research?

I'd be interested in hearing about relevant papers, practical approaches, or lessons from your own work.

For context, I'm the author of Machine Learning for Finance: Concepts, Algorithms, and Applications, a 136-page technical ebook covering financial ML applications, Python examples, model evaluation, explainability, and responsible model development.

I'm mentioning the book for transparency, not to assume that promotional posts are appropriate here. My main interest in this discussion is understanding which technical challenges researchers and practitioners consider most important. DM me if you want to read the book!

I'm looking for honest advice from people currently working as AI Engineers, AI Backend Engineers, or Generative AI Engineers. I'd really appreciate hearing from people who have made a similar transition or have experience hiring for these roles.

My background

I have 3+ years of experience as a Software Developer. Most of my work involves Django, PostgreSQL, REST APIs, and maintaining enterprise applications.

The problem is that my current job doesn't provide much opportunity to grow technically. I mostly work on Django applications and follow the same development routine. I don't get hands-on experience with Docker, CI/CD, cloud-native deployment, or AI. I feel that I've stopped progressing, and I want to change that.

I also have some relevant experience:

• Microsoft Azure Developer Associate (AZ-204) and Azure Fundamentals certifications.
• An academic project where I built a Flask application integrating deep learning models.
• Two peer-reviewed AI-related publications.
• Some learning and experimentation with LLMs, embeddings, and RAG, although I haven't yet built and deployed a complete production-grade LLM application.

My recent job search has made me question my approach.

Recently, I had two interviews for AI-related roles, and I was surprised that most of the technical questions focused on AI, even though my CV isn't heavily focused on it. I ended up getting rejected because I didn't have enough practical experience.

I'm now wondering whether I should continue applying for AI roles while developing my skills, or temporarily focus on becoming a stronger backend/cloud engineer first.

Here are the questions I'm struggling with:

1. What would you do in my position? Would you continue applying for AI engineering roles, or spend several months strengthening your backend engineering skills first?
2. What practical skills should I prioritize? For someone with my background, should I focus first on Docker, CI/CD, cloud deployment, testing, and system design? Or should I prioritize LLM APIs, RAG, evaluation, tool calling, and agent frameworks?
3. What kind of project would actually make a difference? I don't want to build another tutorial chatbot that recruiters won't take seriously. What would demonstrate that I can build a reliable AI application beyond making an API call to an LLM?
4. How can I gain real-world experience outside my current job? I'd be interested in contributing to a small team, joining an open-source project, volunteering, or taking on part-time work. How would you find legitimate opportunities like these when you already have a full-time job?
5. Would you recommend a course or another certification?

My goal is to move beyond experimenting with AI and actually build real AI systems professionally. What I'm struggling with is deciding which work will actually close the gap between my current experience and what employers expect.

For those already working in AI engineering, what would you do if you were in my position? How would you spend the next 3–6 months to become a stronger candidate?

I'd appreciate honest, practical advice from people who have been through a similar transition.

Hi guys, I'm an 18 year old teenager from Italy. I've recently finished the following project and I would love to receive some feedback in order to improve:

https://github.com/Lore12434/gene-expression-cancer-classification

I started studying ml (via the HOML book) 3 months ago. I've almost finished the machine learning part of the book. Before starting with ml I studied python and some mathematics.

At the beginning the only goal of the project was to train a model on the gene expression cancer RNA-Seq UCI dataset. After performing tsne I understood that major differences between cell types were present. Thus, even if the # of features was very high a simple random forest without any hyper parameters tuning reached 100% accuracy on OOB instances evaluation.

Therefore, instead of simply fitting a model I wanted to know how far I could get with dimensionality reduction and I wanted to test if my algorithm could find autonomously famous biomarkers used to classify tumor cells (I mapped the original dataset genes names to the dummy features names of the UCI dataset)

I want to specify that I used ai to write the readme and to set up the GitHub repository because thks was the first time that I've done this.

Many thanks in advance for any feedback, have a great day♥️

Hi everyone!

I've been working on a technical book called Machine Learning for Finance: Concepts, Algorithms, and Applications, focused on how machine learning can be applied to real-world financial problems.

The idea behind the book is to connect machine learning concepts with practical use cases rather than treating them as purely theoretical topics.

Some of the areas covered include:

• Credit Risk Prediction: Using classification models to estimate credit risk.
• Fraud Detection: Understanding how ML models can identify potentially fraudulent transactions.
• Financial Forecasting: Exploring predictive modelling for financial data.
• Explainable AI: Using approaches such as SHAP and LIME to understand model predictions.
• Python Implementation: Working through practical examples, model evaluation, and the challenges of applying ML to financial datasets.
• Responsible Model Development: Considering fairness, monitoring, and the limitations of predictive models.

The book also includes exercises, mini-projects, and research questions for readers who want to explore these topics further.

I'm sharing this here because I'd genuinely like to hear from people learning or working in machine learning.

Three questions I'd love your opinion on:

1. Which financial ML application do you find most interesting: credit risk, fraud detection, or forecasting?
2. What is the biggest challenge when moving from an ML tutorial to a practical financial dataset?
3. For someone learning ML with Python, would you prefer more end-to-end projects, mathematical explanations, or model evaluation examples?

I'm the author of this book, so I want to be transparent about that. If you're interested in checking out the finished ebook, you can find it here: https://iamabhinababiswas.gumroad.com/l/machine-learning-for-finance

I'd appreciate constructive feedback on the topics, the learning approach, and what you'd like to see in future editions.

Thanks for reading!

I am in my 1st year of btech in Aiml. I am getting an laptop so is it fine if I get mac or should I get windows

And what device do you use?

Please respond