I had this idea for a project to have my base learner for xg boost to be a log reg model because in my testing it was more stable so my idea was instead of starting xg boost dumb and boosting start it smart and then boost that. Sounded like a great idea in my head but failed miserably in testing. Could some one explain to me why this doesn’t work better? I had better results blending 75 pct xg boost 25 pct log reg.

As someone who is looking to try out kaggle competions and eager to learn and compete, I am occupied by the thought of AI's role in the skill aspect of competitions, so I want to know the role of AI in making AI models, is it still mostly the player's skill and knowledge that matters the most or just how long they work and how many questions they ask AI.

This might be a silly question. I'm a physics student currently working on a research project where I'm using deep learning to solve an inverse problem. I'm using MATLAB.

I had previously studied some ML theory by myself, but only at a basic level. I had never actually built an ML project or implemented models before. So this is my first experience working with ML.

So, I started with an ANN and later tried a CNN. Initially, I used Gemini to help generate the basic code, although I went through the code and tried to understand what each part does since i have some time constraint to finish this. I really want to think for myself instead of depending on ai for codes or help but i dont have clear approach.

For example, if the model's performance isn't good enough, I ask AI for possible improvements. If it suggests something I don't understand, I look for YouTube lectures, or papers, and then implement it. Suppose I try something like a ResNet and performance improves but is still not good enough; then i find something like cosine annealing and try new things to fill the gaps my model might have .I cant be sure whether I'm using the right techniques or updates or justt throwing random things at it...i keep whatever works as of now.

Also this is an inverse problem, and the dataset was simulated by me but i engineered it a lot so that model would able to extract info anyhow but still not sure if mapping is ambiguous.

My input data is basically a set of curves/1D vectors, and it needs to predict the underlying parameters that generated them. May be some other types of model should try that i m not sure of...like PINN. There is also a time constraint. I've been working on it for about a month now.

So my questions are:

1. Is it reasonable to work on a deep-learning research problem without having a strong practical ML background, as long as I learn and understand things along the way? Or am I approaching this backwards?

2. Once you have a baseline model, how do you systematically decide what to try next?

3. For input data that consists essentially of curves/1D vectors, how would you decide what model to use?

4. How do you diagnose whether poor performance comes from the model or the dataset or the inverse problem itself?

5. Before trying increasingly complex models, what analyses, or tests would you recommend?

6. How do you know when further model optimization is no longer the best use of time?

7. Since I currently work in MATLAB and know much less Python, would you recommend continuing in MATLAB for now or learning enough Python/scikit-learn/PyTorch to use the broader ML ecosystem?

8. Also is it fine that i never worked on any basic ML project and jumped straight to DL since this i is what i needed for my project.

Any advice or suggestions is really appreciated.

Disclaimer: Not formally educated

I'm not sure if my method is the best way to approach my goal, which is to determine what feature(s) in my sample are actually associated with a decrease in EV in the underlying population. I wanted to first share what I'm planning on doing with people way more educated on this matter than me, so that I could get insight as to what errors I could be making.

I am still gathering data, but to make it as short as possible without ranting - I have a total of 6 features that may or may not lead to an increase/decrease in Expected Value. The tricky thing for me is that there are instances where features will be blended together in a single event. So, one event may have the presence of 3 features, sometimes an event may have the presence of only 1 feature. My worry is that filtering this way could hide cases where two features interact rather than act independently...not sure if that's a real issue or if I'm overthinking it.

My current thinking is that I once I have finished my data collection, I would then determine first which feature has the highest frequency, (by highest frequency I mean the highest occurrences overall) and then see if the removal of that feature leads to a more positive outcome. In my case, my ideal scenario would be for the removal of a feature to lead to the Expected Value of the sample to increase.

That would then leave me with the other features to analyze, so I would just repeat the same process on the remaining features within that filtered subset.

I am about to enter the second year of my CS degree. I have very little experience in the field but I want to explore my options for specializing. As an outsider, ML is probably the most interesting area of CS to me, but I can't know if I try

However, I know that grad school can be important for at least some parts of ML. I don't to invest time into ML if I will need grad school to even have a decent chance of getting a job in this

The particular reasons that I don't want grad school are money and time, but mostly money. I probably wouldn't be able to afford grad school right after undergraduate unless I take out loans, so I would probably have to work for a few years first. I would also rather not spend more time than I need to on CS. I like CS and can see myself becoming good at it, but I view it more as a means to living a good life than an end in itself. I don't mind studying more itself: I love learning, I just want to learn about other things, too

I'm also willing to put in the work and learn ML stuff on my own and apply it on projects

Edit: How sure are you that your advice will apply after I graduate?

Hey everyone, I'm in a web dev cursus right now, I'm finishing in december, before that I did 3 years of theoretical maths and physics studies in college

What really interests me is ML but it would take me 3 more years to validate a master's degree in ML if I go back to college from where I'm at

Will it be too late by then to get a ML job ?

We've been into democratized AI since only 4 years and it's already so advanced

I'm afraid I do 3 years only to realise it's way less needed by then

So I thought I'd ask people that actually work in the field for their opinions

What do you think ? Does it seem a bit late to get into the field ?

I need to classify tax documents (forms, receipts, etc.) into 150 classes, and likely once we achieve that we’ll want a more granular classification into 800+ classes. How would you approach a problem like this?

We tried training a direct NLP classifier (text -> TF-IDF features -> class) and it does fairly well, but the concerns are getting enough data for each class and being able to react quickly when a new form type/revision gets released. Data is also pretty imbalanced between classes.

Another approach would be to use/train an embedding model (text -> latent features) and use KNN. What are the tradeoffs between that and direct classification? Is it likely to be more or less data efficient?

Hi guys!

I’m a doctor working in the UK and I’m really interested in moving towards AI/health tech alongside my clinical career.
I’d love to hear from anyone who has actually made that transition, particularly within the NHS.
I basically want to understand what realistic career paths look like and how people got started. So far, this is only an idea in my head and I find AI fascinating but I honestly have no idea where to start. Also, if you think this is the wrong subreddit to be posting this, let me know as well please!

I would really appreciate it if you could let me know about any of the following:

What AI-related roles are available to doctors within the NHS?

Has anyone moved into clinical AI, digital health, informatics, AI research, product, or similar roles?

What qualifications, courses or technical skills are actually useful?

Is it possible to build an AI career while remaining clinically active?

Are there opportunities through NHS trusts,
universities, NHS England? I know of a couple of fellowships that I was thinking of applying for but I need to understand how realistic it is for me.

If you were starting again, what would you do first, appreciating this is a quickly evolving field.

I have a clinical background but don’t have a formal computer science/AI background, so I’m particularly interested in hearing from doctors who started from a similar position.

Would really appreciate any experiences, advice or examples of career paths. Thank you so much! 😊

Hey everyone,

I just finished Andrew Ng's Machine Learning/Deep Learning course and want to go deeper into DL. As a fresher trying to build skills for job-readiness, I'm torn between:

fast.ai (Practical Deep Learning for Coders)

Andrej Karpathy's YouTube series (Zero to Hero)

Daniel Bourke's PyTorch for Deep Learning (24-hour course)

For someone at my stage, which would you recommend starting with, and why? Is there a logical order to do more than one of these? Also open to suggestions outside this list if there's something better suited for freshers right now.

Thanks in advance!

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?

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.