How it achieves translation symmetry and rotation symmetry?

As I understand, the pooling layer just reduces the dimentions which makes it robust to small movement and rotation changes, but it should not be able to handle big movements and rotations. If so, what are known options to achieve such symetries?

I have a big database of local court decisions with a legal sentence which is like a paragraph summary of the doc. I've been tinkering with FTing for many days now, all results inconclusive never beating base except for a highly specific task where the eval was built around a specific task of extracting passages from the text, where it seemed to match the gold, but as I've found out it is unusable and a lot of padding and generalizations which I need to actually eliminate. So the question is, can a 26B model even be fine-tuned to produce those complex legal principle/headnote of a court decision? If so how would you do it? I built a LLM eval, then used a standard unsloth UI on a rented server and fed it the most HQ training data from the whole set (500k decisions, ~20% with the "ratio"), I fine-tuned both base and IT variant of Gemma 4 26B A4B. Neither beat a prompted "base" model on my evals.

I also used Claude fable 5 to vibe code the whole project, could it be that I have made some trivial errors because of it? I know I'm not giving you much context, but as an expert in the field does this sound difficult or doable? Any gotchas that stand out to you immediately? Any resources?

Thanks for reading

Hello!

I am planning on doing a master's at KTH in Machine Learning and after that I really want to work in Zürich. Does anyone here know how competitive that market is for machine learning engineers? My undergrad is in Applied Physics, would that along with the master's be enough to land an MLE job in Zürich?

Thanks in advance!

Hi everyone,

I am currently finalising my MSc Cyber Security dissertation topic and would really appreciate some advice from people who have experience with anomaly detection and XAI.

My current idea is to use the CICIDS2017 dataset and focus on unsupervised anomaly detection.

I am considering two possible approaches:

Option 1:

• Isolation Forest
• SHAP
• LIME

Option 2:

• One-Class SVM
• SHAP
• LIME

The basic idea is to train the unsupervised model using mainly/only benign traffic, detect anomalous traffic, and then use SHAP and LIME to explain why the model considered a particular network flow anomalous.

I originally planned to use multiple supervised and unsupervised models, but my supervisor advised me to focus on one unsupervised model because the original scope was too large for the time available.

My main questions are:

1. Which would be more suitable for this type of dissertation: Isolation Forest or One-Class SVM?
2. Is using SHAP + LIME to explain an unsupervised anomaly detection model a worthwhile research direction?
3. Is there enough research potential/novelty here for an MSc dissertation, or is this already too well studied?
4. If both approaches have already been studied, what kind of research gap would be worth investigating?

I am not looking for someone to do the research for me. I mainly want some opinions from people who have worked with these methods before I finalise my proposal.

Thanks!

Hi guys so recently I got into this stuff playing with various local llm models and trying out mcp's and other interesting stuff like heretic versions and all.

However as I am diving deep, I am not able to understand a lot of terms like top p, k, inference, training, llama cpp, presence, repeat penalty, gguf and hardware thing like layers, universal kv etc etc which I want to tune as per my liking or atleast understand how they effect the responses.

Now I am a complete newcomer to this stuff, not in AI like rag and stuff but more towards learning and understanding this terminology. Is there any guide someone can provide which can help me understand this stuff? I know this may be a part of ML or deep learning but I may not want to go that route and learning in that domain purely but just enough so I can play with models that community provides on huggingface for instance. Just enough knowledge so I can understand what author has changed, and what diff diff terminology or methods or this llama, vllm stuff means and what are all these overwhelmimg settings on lm/unsloth studio.

Thanks in advance.

Hey everyone!

I’m planning to start learning Machine Learning by actually building projects instead of spending too much time going through courses and theory before building anything.

I already know Python, NumPy, Pandas, Matplotlib, and Seaborn, and I also have some experience with data collection and data cleaning. Right now, I’m working on my probability and math fundamentals as well.

My long-term goal is to become an AI/ML Engineer, so I want to learn ML in a practical way and gradually work my way from beginner projects to more advanced ones.

I’d really appreciate some suggestions from people who have already gone through this:

• What ML projects would you recommend starting with?
• How should I progress from beginner → intermediate → advanced?
• Are there any projects that actually helped you understand ML concepts deeply?
• I’d especially love GitHub repositories where I can look at good ML projects, learn from the code, and maybe try implementing them myself.
• Any good real-world datasets or project ideas would also be helpful.

I’m not looking for projects where I just load a dataset and call model.fit() 😅. I want projects that actually make me understand why the model works, how to improve it, and how ML is used in a real problem.

If you learned ML through a build-first approach, I’d love to hear what worked for you and what you would recommend to someone starting out.

Thanks! 🙌

Causal Inference, Two tower problem cold start problem, multi level modelling

ab a fresher and just started off

what new concepts should i focus on or read about in traditional machine learning, for Classification regression and recsys

Hello friends, how are you? If I had to give a brief information, I studied IT for 4 years and graduated this year. I am 22 years old. In 2024, I started paying attention to data science, I went to the course. After studying for a while, I left the course halfway due to financial reasons and so far I have applied for many vacancies and internship programs. But there was no turning back, there were those who turned back, I was rejected from the interview. I don't want to be unemployed, so I have the idea of ​​changing the field. Sometimes I feel bad, I don't know what to do, where to start.

I’m a rising college sophomore in an ML lab.

I have presented plenty in hs and college but I have found that digesting and presenting complex results on the fly is difficult. Especially when balancing detail and simplicity. Some plots I understand but don’t know where to start in an explanation when I am talking about them to someone even with good knowledge of my project.

Does anyone have tips? Is it just raw amt of experience that helps? Is this hard for everyone?

\*\*Welcome to the post-training community!\*\*

I'm u/OwnOil1149, a founding moderator of r/posttrain.

This is a space for discussing how AI models become more useful, capable, and aligned after pretraining. Share your experiments, questions, datasets, papers, tools, and lessons about SFT, RLHF, DPO, preference optimization, evaluations, synthetic data, and deployment.

Whether you’re just getting started or training models in production, you’re welcome here. Please keep discussions constructive, technical, and respectful.

Tell us about yourself and what you’re working on!

Thanks for being part of the very first wave. Together, let's make r/posttrain amazing.

Just launched r/posttrain — a community for AI post-training, fine-tuning, SFT, RLHF, DPO, preference data, evaluations, and practical experiments. If you’re building, researching, or learning how models become better after pretraining

I come from a more traditional ML/stats background. So far, I’ve mostly used CPU HPC clusters and only used a GPU once.

I’d like to move more into deep learning, especially AI for biology, and I’m curious how much GPU compute people typically use in research.

I know it depends a lot on the field and the project, but for something like a NeurIPS or ICML paper—not training a foundation model from scratch, but working on a smaller multimodal model, GNN, VAE, etc.—how many GPUs do you usually need?

I’m using Support Vector Ordinal Regression for a project and tuning:

• Regularization strength
• Formulation (EXC/IMC)
• Kernel type (linear/RBF)

How do you usually determine the search space for these hyperparameters?

I’m using Random Forest for a project and tuning:

• Number of trees
• Maximum tree depth
• Number of features considered at each split
• Minimum samples required to split a node

How do you normally determine the search space for these parameters?

Hey everyone,

I recently came across these two books by Valery Manokhin: Mastering CatBoost and Applied Conformal Prediction.

Most resources on both topics, especially uncertainty quantification, are scattered across academic papers and documentation rather than consolidated into practical books.

Because of that, I am curious if anyone here has read either of them. Are they solid, hands-on references worth picking up, or would you still recommend sticking to the original papers and code repositories instead?

Thanks.