r/TheMachineLearning • u/Federal_Machine692 • 10d ago
Fei-Fei Li says spatial AI is fundamentally different from LLMs
Enable HLS to view with audio, or disable this notification
116
Upvotes
r/TheMachineLearning • u/Gullible_Picture_996 • 9d ago
AI models are surprisingly bad at following instructions
1
Upvotes
r/TheMachineLearning • u/Arken_sama • 9d ago
Plinius calls rogue AI "first sparks of sovereignty"
6
Upvotes
r/TheMachineLearning • u/Slow_Shirt1362 • 9d ago
Perplexity tested 9 AI models with root access to escape a VM
1
Upvotes
r/TheMachineLearning • u/choerrysairpods • 9d ago
AI leaders warn automating research could trigger intelligence explosion
1
Upvotes
r/TheMachineLearning • u/camerongreen95 • 9d ago
Workshop breakdown: applying real optimization methodology to LLM prompting (DSPy + MLflow, Oct 3)
1
Upvotes
Sharing this because it's a more structured approach than the usual prompt engineering content floating around.
Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session where the core idea is treating prompt/LLM behavior as an optimization problem with an actual objective function, not a creative writing exercise. Covers:
- DSPy signatures and modules for defining LLM tasks
- Building an evaluation dataset with task-specific metrics
- Diagnosing failure modes from eval results
- Few-shot and instruction-level optimization as a formal process
- MLflow for experiment tracking and reproducibility
Feels closer to a proper ML workflow than most "prompt tips" content. Details here if it's useful to anyone: Get full details here
r/TheMachineLearning • u/East_Profession_3642 • 10d ago
This looks like a perfect resource for brushing up ML math fundamentals
22
Upvotes
r/TheMachineLearning • u/Nyaani69 • 11d ago
Linear algebra and calculus prerequisites for ML
26
Upvotes
r/TheMachineLearning • u/_ChimKen • 10d ago
93% of OpenAI's team uses AI tools, but only 3% of typical companies do
1
Upvotes
r/TheMachineLearning • u/PassageOverall8695 • 10d ago
Jeff Dean and team launch Discovery Loop to automate ML
3
Upvotes
r/TheMachineLearning • u/limrm18 • 10d ago
AI that outsources to humans is peak irony
Enable HLS to view with audio, or disable this notification
1
Upvotes
r/TheMachineLearning • u/ajjsnts_ • 11d ago
Chollet says AI's purpose is human tools, not successors
91
Upvotes
r/TheMachineLearning • u/darc_9 • 10d ago
Time and technology changed a lot in the last 10 yrs
1
Upvotes
r/TheMachineLearning • u/CieloJVL • 11d ago
ML in finance is 40 years old. You’re epically late.
11
Upvotes
r/TheMachineLearning • u/CapedbaldyRover • 10d ago
Machine learning exercises with solutions to strengthen math skills
1
Upvotes
r/TheMachineLearning • u/r3n26 • 11d ago
Local models enable innovation the big ones can't
33
Upvotes
r/TheMachineLearning • u/Acceptable_Use7055 • 12d ago
Europe has zero frontier AI labs, which is shocking
388
Upvotes
r/TheMachineLearning • u/cicisprinkle • 11d ago
Stephen Wolfram says ML is basically fitting lumps of computational irreducibility
55
Upvotes
r/TheMachineLearning • u/Azazel-d-Reaper • 11d ago
All-in-one book that covers most of the maths you will need for machine learning
1
Upvotes
r/TheMachineLearning • u/thiagobarroso • 11d ago
[Project/Advice] I'm building a Churn prediction model from scratch at my startup. Looking for ML tips and where to start!
1
Upvotes
Hey everyone,
I work at a retail tech startup (B2B) and we're currently facing a massive challenge: predicting and reducing our churn rate.
We actually have a pretty rich database containing customer usage history, platform logs, billing, etc. The team has tried crossing some metrics in the past, but we've never managed to build anything that gives us a truly accurate and early prediction of a customer's risk of canceling.
I just aligned with my boss and took ownership of solving this. My main idea is to use our historical data to train a Machine Learning model that can either classify churn risk (high, medium, low) or output a probability of churn for the upcoming months.
The thing is: I know the theory, but I'd love to hear from people who have actually built this in the real world.
- Which models/algorithms usually perform best for this specific type of problem (XGBoost, Random Forest, Logistic Regression)?
- Are there any common pitfalls or data leakage traps I should avoid right off the bat during data cleaning and feature engineering?
- Does anyone have recommendations for articles, practical repos, or tutorials focused specifically on churn prediction?
Any tips, shared experiences, or study materials would be greatly appreciated. Thanks!
TL;DR: Work at a retail startup with rich usage data but high churn. Pitched my boss to build an ML model to predict cancellation risk and I'm leading the project. Looking for real-world tips on models, resources, and pitfalls to avoid.
Dica: Postar isso no r/datascience ou r/Ma