New on the TrainLoop blog: MAE, MSE & R² — Making Sense of Model Errors We break down Mean Absolute Error (MAE), Mean Square Error (MSE), and R-Squared -- three core metrics that shape how we judge model performance. Link to blog -- trainloop.ai/blogs/simple-e…
A bite-sized explainer on how LLMs learn - end to end. Core ideas without the math. Follow @TrainLoop_ai for our plain-language blog series that dives deeper into each concept.
With @TrainLoop_ai , AI model training outcomes are predictable, repeatable, and sustained --- not blind trial-and-error.
Precision vs. Recall 🤔 Always confused between the two? You’re not alone. We broke it down in plain English + a 2-min “Wild Fire” game 🔥 After this, you’ll never mix them up again : 👉 trainloop.ai/blogs/importin…
AI model training isn’t about getting lucky -- your competitive advantage depends on it. With TrainLoop fine-tuning, you trade chance for control.
Remember the last time someone brought up "Mean Squared Error" in a conversation, and you nodded your head like you knew what it meant? Learn the concept in depth -- trainloop.ai/blogs-landing #learn_the_basics_with_trainloop
New office at @trainloop_ai Comes with cardboard and bubble wrap
So many good-looking things in one frame. Also, here’s our new office in North Beach. If you’re around, come say hello.
Have you ever spotted a four-leaf clover? 🍀 We’d love to hear your story if you have!
How accurate is your model? Before you think about it, Here's another one - what is 'accuracy', really?
The coffee machine is in the house! And we have started running some experiments. We are a Research Lab after all!
Model evaluation is the broccoli of Machine Learning. 🥦🥦 At least we made it simpler -- our evals framework is now open-source → github.com/TrainLoop/evals
So many good-looking things in one frame. Also, here’s our new office in North Beach. If you’re around, come say hello.
The intern now runs one mile every day at 7 am with @mlpierce22 and comes to the office by noon. We'll update when he hits the 1.1-mile target.
“I’m excited to tell my girlfriend that I run now. She’s been trying to get me to do this for the last year.”
When you fine-tune your AI model without a “map”, it will wander. Random tweaks = random outcomes. TrainLoop gives your fine-tuning process structure, direction, and control - so you land where you intend, not where the currents take you
ODML is tricky, MLX-> CUDA is a great first step.
MLX supporting CUDA might be key to Apple's Siri strategy, here's why...
Love this explanation on why AI models always generate outputs that seem good but don’t get anybody excited. This is the main reason why custom models are the future. A one-size-fits-all solution actually fits nobody
Every single RL paper for image-gen I've seen honestly the outputs look like SLOP. my gut is that the mean aesthetic preference, whats optimized for, is not a good preference, its kitsch. Is there a way we can sample individual aesthetic targets?
Cannot say this enough Working in robotics right now is probably one of the highest ROI bets in history It’s like web in the 90s, mobile in 2007…
🍿 OoOh we’re getting CODEX in ChatGPT
What can duckings teach us about neural networks? Turns out more than you think: an excerpt on Imprinting from "Why Machines Learn" by @anilananth 🦆 1/6
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