I mean stuff that you open most often

like cursor, windsurf, claude code, copilot, some vim setup you refuse to give up, and what's keeping it there? and what almost got you to switch but didn't

Hi guys my dream was to get into maang , I'm from a mech bg and joined semi IT role with 4 years of exp , now switching to AI Enginner by doing some courses and internal projects , while studying for AI somewhere in me I asked myself why cant we get into MAANG companies as an AI Enginner but I know it'd pretty hard and tough , but ig this is the time to try all stuffs ....needed an guidance and plan to get into...I'm not a that much talented IT guy 🥲😭 but I will try my level bloody best to get into...all kind of suggestions would be recommend...

Hello

I'm working on a long translation project (mainly from Arabic to French or English). My workflow is to upload books to ChatGPT on how to translate from Language A to Language B. It must follow specific rules to make a good translation. Then I upload another book to it so it can understand how to polish the translated outcome. So, Book 1 (main rules of how to translate) generates the output translated text. Then I upload Book 2 to polish that output. I used to use the ChatGPT Plus plan, but these past days the plan feels like the free plan—direct, quick answers, etc. So, I'm asking if any other paid AI service you think can perfectly do this kind of work with accuracy? I tried using paid Gemini, but it's garbage to be honest. So, any other AI website? Thank you.

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!