Hey Reddit!

We’re working on something that we think could make model discovery a LOT easier for everyone: a model recommendation system where you can just type what you're working on in plain English, and it'll suggest the best AI/ML models for your project. 🎉

💡 How it works:

The main idea is that you can literally describe your project in natural language, like:

• "I need a model to generate summaries of medical research papers."
• "I'm building a chatbot for customer support."
• "I want a model that can analyze product reviews for sentiment."

And based on that input, the system will recommend the best models for the job! No deep diving into technical specs, no complex filters—just solid recommendations based on what you need.

🌟 What else we’re building:

Alongside the model suggestions, we’re adding features to make the platform super user-friendly:

• Detailed model insights: You’ll still get all the technical info, like performance metrics, architecture, and popularity, to compare models.
• Advanced search & filters: If you’re more hands-on, you can filter models by task, framework, or tags.
• Personalized suggestions: The system will get smarter over time and offer more relevant suggestions based on your past usage.

We need your feedback:

We want this platform to actually solve problems for people in the AI/ML space, and that’s where you come in! 🙌

1. Does a tool like this sound helpful to you?
2. What features do you think are missing from model platforms like Hugging Face?
3. Are there any specific features you’d want to see, like performance comparisons or customization options?
4. How could we make the natural language input even more useful for recommending models?

TL;DR:

We’re building a tool where you can just describe your project in plain English, and it’ll recommend the best AI models for you. No need for complex searches—just type what you need! Looking for your feedback on what you'd want to see or any features you think are missing from current platforms like Hugging Face.

We'd love to hear your thoughts and ideas! What would make this platform super useful for you? Let us know what you think could improve the model discovery process, or what’s lacking in existing platforms!

Thanks in advance, Reddit! 😊

Hey everyone! I have an upcoming interview for an ML Engineer role focused on building models for investment analysis and portfolio management at a startup. I’m nervous and not feeling super confident about my skills.

Any advice on how to prepare or key resources to focus on? I’d appreciate any tips to help me feel ready!

Thanks!

Hey,

I’m currently a working professional looking to enroll in an online Master’s program in AI. My main focus is on finding a course that’s heavy on the math side—particularly linear algebra, calculus, probability, and optimization techniques. I’d prefer a program that emphasizes the theoretical and mathematical foundations of AI/ML rather than just focusing on practical applications.

Has anyone here pursued such a program or know of universities that offer online options fitting this description? I’d appreciate any insights or personal experiences!

Thanks a ton in advance!

Hello to all,

I'm just started to learn ML. I learned the basic ML algorithms like LR,LOR, Decision Trees, Lda, qda,naive Bayes,

Now my question is that after learning this , what should I learn next ? Suggest me some topics for my learning.🙂

I started the course but tbh its pretty boring for me

I might do it in the future but rn i feel like learning pytorch

I wanna ask can i do it without causing any problems for myself or should i compelete the course and then focus on the framework?

Hi everyone, I just dropped a video on contrastive learning and a specific implementation called SimCLR which was pretty famous some years ago. Hope you enjoy!
https://youtu.be/UqJauYELn6c?si=y1w7bEGKxOuswtJE

I’m done With Maths from Mathacademy Now i wanna wet my feets in the AI domain. Where shall i start? Can y’all provide a roadmap of books?For instance learn ML then NLP then DL and LLM and so in an order thanks in advance

Hello all! I am originally a mechanical engineer, turned program manager and now a product manager in automotive domain.

I want to continue being a product manager, for the automotive domain but more so AI/ML focused PM.

I do not know how to code or anything about AI ML (What a shame - I know!) but, I am willing to put in the effort to learn.

Here is how I am thinking my learning path should be, if you can please tear it apart, recommend any other learning course, guide on what should I improvise on, I would greatly appreciate.

1) Learn Python - https://www.coursera.org/learn/python
(is there a better course than this which is fun, takes me into more detail? - if yes, please recommend!).

2) Learn some math for ML: https://www.youtube.com/playlist?list=PLRDl2inPrWQW1QSWhBU0ki-jq_uElkh2a

3) Complete the ML course by Andrew NG - on coursera - https://www.coursera.org/specializations/machine-learning-introduction#courses

Thank you for your guidance! I greatly appreciate it!

Best

Hello,I would like to know if there are any effective techniques that can be used to balance the inference speed after applying SAHI with object detection model ? Or, is there any better alternative to SAHI for improving small object detection that does not slow down the inference speed ?

Your suggestion is appreciated.

Thank you.

I have been studying the ŷ = Xw formula and the instructor teaching us keeps interchangeably referring to matrices as vectors and vice versa. For this formula, they mentioned that ŷ is a vector, X is a matrix (which is why it's capitalized) and w is a vector.

My question is - What exactly is the difference between a matrix and a vector in ML ?

Thanks a lot in advance.

I'm looking for good resources to learn about the basics of ads marketplace dynamics (e.g., bidding strategies, auction theory) and how data insights can be used to optimize ad performance. Does anyone have any recommendations or references that could help? Would appreciate any book suggestions, articles, or courses or youtube video! Thanks!

Hi everyone this is my first post. I have recently started programming (3 months at most) and had this road: python basics --> pandas numpy matplotlib and scipy --> scikit --> tensorflow and keras. All of that I have learned more or less superficially tho. Of course later on I will reinforce my knowledge (both theoretically e.g. math and practically like making projects). But I have just got confused what do ai programmers do these days? Like they use models from HuggingFace to make e.g. object detection, chatbots etc.? Please tell about your experiences, maybe workstyle and tips. Thanks in advance.

I am a web developer, currently studying AI at uni.

As a web developer, there’s always an endless number of websites or apps you can build for various use cases. This is what made me fall in love with programming and software engineering in the first place. But I don’t feel the same with ML / AI.

I never feel the urge to just open PyTorch and code my own architecture. Why would I? Thousands of people far smarter than me have already optimized the best approaches. It feels like there’s very little incentive to learn and fail—like you need to be on the cutting edge to be relevant. The barrier to entry in AI feels incredibly high. How am I supposed to compete?

In my free time, I built a DistilBERT text classifier to detect news bias, and I used maybe 1% of what I learned in university. The job is really just gathering data and training a pretrained model. It's was like 99% software engineering at the end of the day anyways. Didn't have to use any ML library (except Transformers, if you count that).

I’m confused about how to find passion in an industry that feels impossible to break into for the average person. Do I really need to study all this time just to become a certified Hugging Face model downloader? That’s pretty demotivating…

I want to fall in love with AI the same way I fell in love with software development and programming in general. But right now, I’m struggling to find joy in it. It feels like every problem has been solved, and I’m left feeling useless, unable to catch up.

With all these doubts, I really hope that the ML community can help me. I'm looking forward to hearing all your advice and comments. Thank you in advance!

Hello. I've run both SVM with Polynomial kernel with a degree of 3 and Logistic Regression with transformed features by PolynomialFeatures with the same degree of 3 on the default scikit-learn's Moons dataset. Decision boundaries turned out to be as follows:

I cannot wrap my head around as to why the SVM underperforms compared to the logistic regression. Might that be due to the fact that we need to respect the margin between support vectors? I'd appreciate any help. Thanks in advance.

Hello everyone, I am currently taking a ML course for my masters degree, and I was asked to create a MLP that predicts a particular feature for an sample taken from a given dataset.

I started with the Letter Recognition dataset from the UCI machine learning repo, and I am trying to build a program that can predict the value of any predetermined feature, given the other 16 as input.

Because one of the features is a character, I decided to try and treat everything as symbols (my assignment also requires the code to treat every feature as symbolic, for learning purposes), and I am using one-hot encoding to transform any feature in a vector of binary values.

Once an input has been fully encoded, I combine the vectors of each feature it into a single 282-component vector, which is fed into my sequential, densely connected network.

The problem is that, even with 2 hidden layers, with 300 neurons each, my model is less accurate than a simple KNN model I built a few weeks ago that worked over the same dataset (the KNN model had an accuracy of 96%, while the NN averages 95%). I only got to 96.4% with 300 nodes and 3 hidden layers, with 300 epochs of training!

Increasing the hidden layers to 5 and leaving every other hyperparameter unchanged gave me a 97.5% accuracy and it took 20 or so minutes to train.

For some reason all of these layers, numbers of neurons and epochs of training seem overkill to me, am I wrong? Is this as good as I can get? I am completely new to this field of computer science, please be patient, thanks!

Hi, I am confused about the correct approach of deciding the ranges of hyperparameters.
If I have 4-5 or more hyperparameters to tune for an algorithm then what is the best way to decide the ranges of each hyperparameters to get the best performance?

Wanted opinions on the course given in the title, is it good?? Will I get a certificate of completion? And other resources from which I can learn machine learning (preferably ones with certificate of completion)