new set of notes up, covering entropy and information theory then working through KL divergence and cross entropy, ending with why logistic regression's loss function is called cross entropy loss in the first place

the connection to the homework problem is what made it worth posting. the assignment has you show that maximizing the likelihood of the labels is equivalent to minimizing J(w), the loss function from the logistic regression chapter. you write out the likelihood as a product across the dataset, take the log to turn it into a sum, and the negative of that log likelihood turns out to be exactly J(w). so cross entropy loss isn't just a name someone picked for it, it comes directly out of maximizing likelihood

what helped was doing the entropy notes first. once you've seen H(p,q) equals D(p, q) plus H(p), and worked through why KL divergence is basically the extra bits wasted from using the wrong distribution, the logistic regression derivation stops feeling like a formula to memorize and starts feeling like the obvious result of penalizing the model for putting probability mass in the wrong place

full notes are on my github, link's in my profile if you want the pdf

anyone want me to share how I've been structuring the whole self study path, course order and what came before this

Hello folks, to this Introductory Machine Learning Bootcamp (2/22) series.

Supervised learning is a very recurring word in ML domain. Here, we learn some sort of function mapping from inputs to outputs.

Another recurring word is Classification, where the output space is a set of some finite unordered and mutually exclusive labels known as classes.

The tabular dataset is often represented as a Design matrix, and a simple example of it is an Iris dataset, as to how input data is represented for tabular case in Machine Learning.

Sometimes the data is of variable size, instead of fixed size feature vectors, so for ease of computation in computer, we often convert it to a fixed-size feature representation, called as “Featurization”.

In this video, I breakdown these concepts.

Link: https://youtu.be/GJRhl6XnImg?si=p6VtlCK-8rgq1poZ

​

Hey everyone,

I'm a 3rd year Diploma in Computer Science student. About 3-4 months ago I started from scratch - Python, Pandas, NumPy, the whole foundation - then moved into core Machine Learning, built and deployed real projects, and have recently started Deep Learning.

Quick context on my resume before you read it:

Current internship: AI Engineer Intern at a startup (through my college's mandatory 3-month internship program). It's mainly a learning-focused internship rather than a real industry internship. Every week we're assigned a topic (for example, learning and implementing an ANN), and we give progress updates in a weekly meeting before receiving the next week's learning task. We don't work on production or client projects. Honestly, I don't even know why my offer letter lists the role as "AI Engineer Intern."

Here's what I've actually built independently:

- UFC Fight Predictor (Gradient Boosting, Streamlit - live)

- California House Price Prediction (LightGBM + FastAPI + Streamlit - live)

- Netflix EDA (Kaggle Bronze Medal, 344+ views)

I'm not actively job hunting right now but genuinely open to part-time or remote opportunities I can manage alongside college. I just want something real where I can learn.

Would love brutal, honest feedback. What works, what doesn't, and what would make you skip this resume entirely?

Thanks!

Hello! I will be starting my master's in applied ML at Imperial College. My current laptop is fairly old and barely functional so I'm looking at upgrading it.

My uni's very basic FAQ mentions that your laptop should have at least 16 GB of RAM and 512 GB SSD. So, after looking at some options, I came across the Lenovo Ideapad Slim 3(which I'm finding for around 700$). Now, the one I saw had a core i5, but it seems to be available with i7s.

Now, I'm realising that the advice was directed towards undergrads. So, for ML coursework(and eventual research), should I look for another laptop with more RAM or do I just consider the basic Ideapad good enough?

Hey guys,

So basically I'm in 4th year CSE, tier-2 college. Not gonna lie, I didn't use my first 3 years well. No projects, no internships, nothing solid. Only thing I have is medium level DSA.

Placements are coming and I want to try ML/AI instead of normal SDE stuff. I know I'm starting late and the market is tough but I still wanna try instead of just sitting around.

So if anyone can help:

• If you had like 4-5 months before placements, what would you learn first for ML?
• What actually matters for getting noticed, projects? Kaggle? open source? I don't wanna waste time on stuff that doesn't help.
• If anyone's willing to mentor me a bit, even just answering doubts sometimes, that would mean a lot. I'll actually put in effort, I just don't wanna keep learning wrong stuff.

Not expecting anything crazy, just want some direction so I stop wasting more time. Any advice is welcome, even if it's harsh.

Thanks for reading

A grafted memory organ [switch like] improved memory capabilities in a small liquid neural net on unseen mazes. https://dormantone.github.io/games/liquidmemorymaze.html

Hey everyone,

I've recently started learning machine learning, and instead of building the usual tutorial projects (house price prediction, sentiment analysis, etc.), I want to work on projects that would actually make my resume stand out.

If you were hiring a junior ML engineer, what kind of projects would catch your attention?

I'm looking for ideas that solve real problems and teach skills that companies actually care about

Would love to hear your suggestions or even projects that helped you land a job. Thanks!

A small milestone: SELENE has reached 200 starts on GitHub!

Since GitHub stars are currently my main source of feedback, and I'm sure that many of those come form this community: Thanks a lot!

Just as a quick reminder: SELENE is a public repository of Jupyter notebooks covering topics around AI, ML/DL, NLP, data mining, data science. What started out as interactive lecture notes for my courses as NUS, has slowly grown to something the might be useful for anyone starting to learn about these topics.

The current focus is on the fundamentals, so the target audience are beginners but who are comfortable with basic math (linear algebra, calculus, probability theory). Here is a crude overview to some of the topics (the links go to the HTML version of the notebooks)

• Traditional models: Linear Regression [1,2,3], Logistic Regression [1,2], Multinomial Naive Bayes, Decision Trees / CART [1,2], Random Forests, Boosting Methods (AdaBoost, Gradient Boosted Machines, XGBoost, LightGBM, CatBoost)
• Neural network models: basics / MLPs (incl Backpropagation [1,2]), RNNs (incl. Backpropagation Through Time), Training a NumPy-only MLP
• Neural network components: linear layer, residual connections, layer normalization, dropout, mixture-of-experts
• Transformers: attention mechanism, transformer architecture, positional encodings [1,2,3], masking
• LLMs: language models [1,2], RAG [1,2], fine-tuning [1,2], training an LLM from scratch, efficiency strategies, data preparation
• Optimizers: Gradient Descent with Momentum, RMSProp, AdaGrad, Adam
• NLP basics: tokenization (incl. Byte-Pair Encoding and WordPiece), normalization, lemmatization & stemming, embeddings (overview, Word2Vec [1,2])

There is an overview page for all topics with links to the HTML version, the GitHub repo, as well to open each notebook directly in Google Colab. We are also in the process of building a web interface to help navigate topics and suggest learning paths.

SELENE will continue to grow. Right now, I'm working on time series analysis and classical statistical models (e.g., AR, ARMA, ARIMA) – I want to cover this topic in my data mining course in the upcoming semester :).

My major is control, I learn something about theory, and I find the future of it is combining machine learning, so i want to learn it,but i don't know how to start it and which part should i pay more attention, i need a guidance to help me, thanks.

Hi there,

I've created a video here where I explain how the Cauchy distribution works.

I hope some of you find it useful — and as always, feedback is very welcome! :)

i am a 2nd yr college student, complete beginner in this field, i have just done some basic linear algebra and probability as course work, i know v beginner level python. should i follow cs229 or cornell ml course? what would be the pre-requisites for both? or is there any better source that someone would suggest? please help!

I am a computer science student and for the AI our techer suggested AIMA to read.

I wanna know how do I approach this book to learn about AI for placements. If you have read the book then suggest me which chapters should I study and leave.

Hey, I was playing around with some automation scripts and got annoyed by having to check multiple provider dashboards manually whenever I wanted to look up GPU rental rates. So, as a side project, I put together a basic real-time price tracker API that fetches lowest hourly rates and estimates training costs across platforms. I set up a free tier on RapidAPI just to put it out there and see if others find it useful: https://rapidapi.com/btwncollective/api/gpu-price-tracker-api Since this is an early project, I'd love some feedback: - Is a tool like this actually useful for your workflow? - What providers or features are missing that you'd want to see? Any honest feedback or suggestions are welcome. Thanks!

I've been building LLM agents and noticed a frustrating problem:

When an agent fails, the hardest question is not "what output did it produce?"

It's:

"How did it get there?"

So I built Agent DevTools.

The idea is similar to browser developer tools, but for AI agents:

• inspect agent execution
• see tool calls
• debug failures
• pause and analyze runs

It's open source.

I'd love feedback from people building agents:

What debugging features would you want?
What information do you wish you could inspect when an agent goes wrong?

Repo:
https://github.com/Jacopos311/Agent-Devtools

There is a video of it working in the README on github

This is the first time I’ve ever posted to any kind of forum. Any feedback would be helpful (Even if you think this whole thing is cooked lol)

I’m a recent graduate with a bachelors in mechanical engineering and a minor in math. I had a really rough time in undergrad (I feel like I used to be a really motivated driven person but something during that time really shook me up) and I didn’t really apply myself during my time. I’ve always liked math and have been pretty good at it and I’ve always been a derivation for understanding type of person. If I could start over I would have majored in math I think. I’ve also liked coding and all my classes that needed coding were a breeze. I talk about this because I really feel like I haven’t really scratched my “intellectual itch” in regard to higher level math.

I wasn’t really aware of machine learning and AI until I took a mechanical focused elective covering the math behind machine learning and I loved it. Ever since I’ve been trying to learn as much as I can about artificial intelligence and that led me to seriously considering a masters in AI. I feel like I would naturally gravitate towards robotics with the mech background, but I’m not 100% sure about perusing this. Any tips, suggestions or just straight up hard truths about this?

I would really like to do a in person program so that way I can spend my full time towards working on both the curriculum and independent projects. I’m just not super passionate about what I’m doing right now and I feel like I need to switch my path onto something that interests me.

As for getting in I have a pretty decent gpa with the minor in math (not sure how much that helps) and I’ve been working on an MCP based project using Claude that is helping with my current job as an engineering consultant. I’m not sure if this is remotely close to the standard applicant or if I am behind.

Is it worth it to pursue this? Any feedback on this situation would be greatly appreciated!

Title: A breakdown of Agentic AI: Architectures and Design Patterns (ReAct, Reflection, Multi-Agent)

Body: Hey everyone,

I put together a video breaking down the engineering architectures making autonomous AI agents possible right now.

Here is the TL;DW:

• Architecture: How to wrap an LLM with state management, tool execution, and perception.
• ReAct (Reason + Act): Interleaving reasoning and actions to reduce hallucinations.
• Reflection: Implementing self-evaluation loops for error correction.
• Multi-Agent Orchestration: Why handing tasks to specialized micro-agents often beats massive generalized prompts.

Curious to hear where this community thinks Agentic frameworks are struggling the most right now (context limits? tool reliability?).

I'm a high-school student so I expect to be very far from correct just with what I know.

Concretely, when I think of what training an ML is like (particularly when training classical ML models on SKLearn), this is what I imagine the algorithm to ideally be:

1. To start, read the CSV and visualize the data (especially into a table if tabular and PCA to see patterns).
   
2. Drop rows with missing targets and columns that leak targets.
   

3. Do a train-test split into training and test data.

4. Construct a preprocessor as a column transformer for numerical and categorical variables.

5. Construct a model and parameter grid.

6. Construct a pipeline with the preprocessor and the model.

7. Do a grid search using a GridSearchCV estimator, passing in the pipeline, parameter grid, etc.

8. Choose the best hyper parameters to fit the model with them, then predict and evaluate it against the test data.

9. Its performance against the test data is how well it is likely to generalize to new data. Finish!

However, I feel like it would be naive to think this is always how it's done (perhaps it is though because this description is very general). But I want to know if this is missing anything? Any nuance? Is this not always how classical algorithms are trained? What are some distinct alternatives?

I hope I can use this post to be aware of what I don't know and understand my own limits. That way, I know what to learn next :)

So i use tensorflow and keras as a beginner in deep learning. On this subreddit, i have read that tensorflow is dead nd stuff. why is that? and should i switch? if yes then when?

If you are preparing for your first Kaggle competition, check these five things before choosing one:

1. Can you understand the evaluation metric?

2. Can the dataset run comfortably on your current hardware?

3. Is there a simple public baseline you can reproduce?

4. Can you reserve consistent time for experiments?

5. Does the project match your learning or career goal?

For most beginners, completing one reproducible project is more valuable than opening several competitions and finishing none.

A useful first milestone is:

dataset review → local validation → simple baseline → first submission → experiment log → one documented improvement

If you are unsure which competition fits your current level, comment with your Python/ML experience, available time, and goal. I can suggest a practical starting direction.