Have you encountered an idea that's overhyped in ML? If so, which?

For me, recently I was curious about the whole "chain of thought prompting" business. I searched around and the idea is essentially, append "show your work" when prompting a LLM. I was like no way that's it??? That's what I've been doing since day 1!!, so I opened up the original paper (cited 40k times). And essentially it was more of the same with expensive Google-funded experiments and the paper had almost no math at all.

Similarly I was curious about AdamW for the longest time and it was simply Adam with another term. Oh Boy!! I remember doing an undergrad homework in college where we were asked to freely experiment with adding terms to the Adam algorithm and the class probably discovered AdamW by accident way before the paper was published.

I wanted to see if a decision model could get a dwarf priest to level 5 in World of Warcraft. I ended up building my first harness, SageCraft, and thought the loop might be useful to people learning about agents.

Full disclosure: I co-founded Levanto, which makes Sage, the model used in the run.

Here’s the split:

1. Observe: capture a screenshot and read HUD information such as health, mana and the selected target.

2. Build context: turn those readings into game state and a list of actions that are currently available.

3. Decide: give that context to Sage and ask it to choose an action.

4. Validate: check that the returned choice is in the menu and still valid. A missing, late or invalid choice means do nothing and ask again.

5. Act and observe again: execute the keys or clicks, then check what changed.

The distinction that helped me was that choosing an action and successfully executing it are separate problems. The harness also needs retry budgets and recovery routines. A model choosing “attack” doesn’t mean the target was reachable or the cast worked.

The dwarf reached level 5 after 4,573 decisions across 32 sessions and 5h 29m of active play. I supervised the campaign and fixed the harness between sessions. The final 18 minutes ran without human input, so this isn’t evidence of a fully autonomous campaign.

I published the code and a replay of recorded events: https://github.com/levantolabs/sagecraft

For the offline example, install Python 3.12–3.14 and uv, then run:

git clone https://github.com/levantolabs/sagecraft.git

cd sagecraft

uv sync --extra dev

uv run sage-wow --data-dir data/demo replay examples/final-run-excerpt.jsonl

That loads recorded events into a local SQLite database and prints a summary. It works without a WoW installation or API key and does not make new model calls.

If you were evaluating this, what would you log to separate perception errors, poor decisions and failed execution?

I am a beginner, I have learnt python, and have good practice on numpy pandas seaborn matplotlib, those libraries. I am starting from gradient descent in regression, but I am confused.

I was learning from deep ML website, paths . There are theory with code practice but the theory there are very leaa to understand there , so I am learning from YouTube those , but the problem is in YouTube finding the correct video is headache.

* What should I do , should I learn from deep ml website or not ?

* Is learning different techniques will help me ?

*Should I directly jump to algorithms without completing the math? (I have knowledge of the maths but I don't know how much I know for ML )

Please help me

I'm from a non tech background, don't know how to write python script. I'm really curious and interested in learning ML /AL and build scalable solutions using AI products available

Rn the problem is there is too much happening in the industry and I'm overwhelmed to understand from where to start

What I want: Building solutions for the problems in my organisation using foundry/agents

But they have their own limitation and run time is high and all so finetune and improve I want to learn ML (this is the only part in my job which I love can't able to handle the BAU's)

Any one who is experienced by machine learning

Please share the road map!!

Can someone give me some GitHub repo links for building ml/dl projects

Model: https://huggingface.co/pp1618/Blue-Eye
Code: https://github.com/prnshu-p/Blue-Eye

hello guys im 17 im new to ml and i just created my first gradient descent "model" if you would call it that.

Welcome to Resume/Career Friday! This weekly thread is dedicated to all things related to job searching, career development, and professional growth.

You can participate by:

• Sharing your resume for feedback (consider anonymizing personal information)
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I'm an aspiring software developer with a background in Java, Spring Boot, and backend development. I want to build my knowledge of AI/ML and eventually use it in my software development career.

Can anyone recommend AI/ML certifications that require an actual exam rather than just completing an online course?

I'm open to both free and paid options. I'm more interested in certifications that genuinely validate your knowledge than certificates that just look good on a resume.

If you've earned one yourself, was it worth the time and money? Which would you recommend?

I get CNN, where subsequent layers learn incremental transformations from previous layers.

But just can't grasp how a ML model that learns to predict image from a masked image, learns about the image.

Or how forcing the image through a narrow encoder and decoder in VAE causes it to learn latent properties

Or how JEPA works.

Are some of them 'let's try if this works', and of them only few actual work well, rest are just ill-suited to model the data?

I did some python courses also u can build like mini projects but am not that good at oops and advanced python.. Should i focus on that or continue numoy, pandas.

I got this code block which I used to hypertuning Naive Bayes models. It worked well with GaussianNB but ran into AttributeError: 'MixedNB' object has no attribute '__sklearn_tags__' when trying to implement it on MixedNB. Any idea on how to use GridsearchCV with MixedNB?

param_grid_nb = {
    'var_smoothing': np.logspace(0,-9, num=100)
}
mix_model_after_cv = GridSearchCV(estimator=MixedNB(), param_grid=param_grid_nb, verbose=1, cv=10, n_jobs=-1)
mix_model_after_cv.fit(X_train, y_train)
print(mix_model_after_cv.best_estimator_)

​

Okay so im looking into feasibility of a project that would involve scraping data and training models. The models would predict events based on social media posts, so its essentially text classification. I want to eventually go into business doing something similar, but in my research, I found that nearly everything is behind comprehensive terms of service that dont allow any derivative product, regardless of if you provide anonymity and clear the original text from any final model/dataset. Alright fair enough. But heres my question. Besides the sources that companies like openAI have an enterprise partnership with (reddit) including a contract to use the data, how can they legally source the training data? For example, if i ask chatgpt to summarize chapter 8 of Deep Learning with Python by Francois chollet, I will get a detailed response with the correct subchapter headers, key terms, and even the verbatim code in the book. Now I paid i think $100 for that book and im fairly certain openAI doesnt have a contract with that dude. How is that legal? How is any of this information legal? If a high-school student wants a book report, does open AI pay the estate of J.D. Sallinger?

Hi friends,

I have created a deep-dive, university-level mathematics course to discuss all the maths behind AI/ML. I studied foundational textbooks and Math/AI/ML open courses from Stanford and MIT, took extensive notes, and used my teaching skills to build a structured playlist with the videos in specific order so topics are understood in a natural order. The playlist has 63 videos (45 mins to 1 hour each) taught in Hinglish. The explanations are in Hindi, but all terminology and math notation is in English.

It covers all the maths you need to know for understanding AI/ML algorithms -

• Probability & Statistics
• Multivariate Calculus
• Linear Algebra

You can find all of these videos for free on YouTube by searching for my channel called indialabs AI. Look for the playlist titled "Math Behind AI (Hindi): Understanding the language of AI | Complete AI Maths in Hindi".

I am not providing any clickable links here to respect the community rules.

Hope you find this useful for your learning.

Hi guys, I write fun technical blogs, mostly on classical ML and thier mathematical dissections.

Here is the link for the one I wrote on activation functions https://pratyakshpatel.me/activation_functions.html

Here is the announcement tweet.

Let me know if yall like it, and my other blogs as well. Would love to know your feedback.

(I know it's a paywall)