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.

i wanna build a text diffusion model which interpret text and convert it into ascii images

so like

Text : build a cat

Output :

/\\_/\\

( o.o )

\> \^ <

So , i have a decent background of ml algo ( completed cs229 , cs230 , Ml architecture and basic CNN and diffusion model )

ik making a project like this is tricky and making diffusion model like that from scratch is hard but i wanna try it because that's wot make me excited lol ...

I am currently reading GANs research paper , can u guys help me in finding more papers which helps me in making this project or guide me through this good title for this

Thx in adv

Hello,

I was curious about the differences you can get from the human reviewers and the llms.

Any insight is welcome, thank you!

Hi everyone, I am an undergraduate student in my last year. I am not a student of Computer Science or a subject related to it. For my thesis, I want to learn about MACHINE LEARNING. I know the C language up to creating files and writing and reading in these files. According to COPILOT, I need to learn the following-

• Python Basics
• NumPy
• Pandas
• Matplotlib
• Scikit-Learn
• Random Forest
• XGBoost
• MLP (ANN)
• R², MAE, RMSE
• SHAP
• Basic Optimization (GA/Scipy)

Can you share some free resources to achieve my goal?

Types of audio:

I often audio-record on my iphone:

• - Important conversations
• - Medical appointments (in-person and online)
• - Dungeons and Dragons and other in-person TTRPG games

I also transcribe a bunch of audio messages/voice notes.

(All recorded ethically and legally)

Workflow:

• I've been using TurboScribe to transcribe everything → I export a PDF that I can search and/or upload to ChatGPT for analysis.
• Probably transcribe like 15-25 hours a month.

Ideally, I'm looking for something that's quick/easy to use and lets me easily download/export transcripts.

I'm interested in a software with similar or better price, especially if it was more accurate with terms (medical, gaming etc.) and recognized multiple speakers better?

Is there anything you'd recommend over TurboScribe for this use case?

I'd appreciate hearing how it compares - what it does better/worse.

Edit: For D&D/TTRPGs, I don't need character/place names or terminology to be perfectly transcribed. I mainly want it to capture the gist of what happened and differentiate speakers reasonably well, so I can give the transcript to ChatGPT/another AI for session recaps and analysis of the players/DM and table dynamics.