Santiago Viquez
@santiviquez.com
ML @ NannyML. Writing “The Little Book of ML Metrics” https://www.nannyml.com/metrics?via=santiago Personal website: https://www.santiviquez.com
What’s the best way to track the progress of my book, The Little Book of ML Metrics? 1️⃣ Visit the book’s repo: github.com/NannyML/The-... 2️⃣ Download the latest digital WIP version. 3️⃣ Start reading while I keep writing. It gets updated every time I push new changes.
It's happening! Join us next week to ask Sebastian Raschka anything! 📅 Date: February 11th ⏰ Time: 10:00 AM – 11:00 AM EST 📍 Register: lu.ma/evqa4rct
Super proud to work at a place that values open science. Four years ago, at NannyML, we invented the first version of Confidence-Based Performance Estimation. Today, a paper about it was published in JAIR. JAIR: jair.org/index.php/ja... ArXiv: arxiv.org/abs/2407.08649
Took me over an hour to fully understand the computation behind the Pair Confusion Matrix. Hopefully, it’ll take you a lot less after reading my explanation in "The Little Book of ML Metrics" www.nannyml.com/metrics?via=...
You can just do many things. Yesterday was my first day at culinary school!
If people don’t think what you do is cringe, then you’re not pushing hard enough. Every person you admire was once considered cringe by someone. A Writer, YouTuber, Founder, Musician, you name it. They all got to where they are because they constantly shared their work with the world. Constantly.
We’re deciding what book to read next in the "AI from Scratch" study group. So far, we have these two: 1. AI Engineering by Chip Huyen 2. Hands-On Generative AI with Transformers and Diffusion Models by Omar Sanseviero and gang Any other suggestions?
• Work hard • Keep learning • Cherish loved ones • Find people who inspire you • Be kind & egoless • Eat healthy, exercise, sleep well • Read & write • Practice gratitude & meditate • Be present • Enjoy food & nature • Don’t sweat the small stuff • Smile =)
First AI from Scratch session of 2025! A big thanks to @carloscapote.bsky.social and Michael Erasmus for their excellent explanations in today's meeting.
Santiago Viquez@santiviquez.com · 2y agoYesterday I forgot to post about our study group meeting 😅 It was an amazing one! @carloscapote.bsky.social walked us through Chapter 5: Pretraining on Unlabeled Data. Next week, we’ll take a short break, but we’ll be back after the holidays to finish Chapters 6 and 7 💪
Forgot to share the news, but here it is: Our NannyML open-source package reached 2,000 GitHub stars! 🌟 Slowly but steadily 💪
Another one from the book. Log Loss (aka cross-entropy loss)! --- If you're interested in more metric descriptions like this one, check out the book I'm writing: The Little Book of ML Metrics. GitHub Repo: github.com/NannyML/The-... Pre-order the book:https://www.nannyml.com/metrics
Which ranking metrics am I missing? In the coming weeks, I'll be working on the ranking chapter for "The Little Book of ML Metrics", and I want to make sure I'm not missing any popular ranking/recsys metrics.
Every time you say "garbage in, garbage out" an ML model dies.
ML Books I'll Be Reading in 2025 📚 1. "AI Engineering: Building Applications with Foundation Models" (Huyen, 2024): amzn.to/4gtQgJo We’ll probably read it in the study group "AI from Scratch."
During the pandemic—specifically, on May 7, 2020—I wrote some goals on a piece of paper. I folded it, stored it in my wallet, and forgot about it. Today, I found it and realized I’ve accomplished all of them.
I wrote a retrospective about my 2024, reflecting on all the amazing things that happened to me—and the not-so-amazing ones. Feeling grateful and extremely excited about 2025! www.santiviquez.com/blog/2024-re...
2024 Year in Review
A review of the year 2024. What I did, what I learned, and what I want to do in 2025.
santiviquez.com
If you're feeling generous and want to buy me a Christmas present while also getting yourself one, hit that pre-order button! 😂 Just kidding—sharing this would mean the world to me too 🫶 📔About the book: www.nannyml.com/metrics?via=...
The Little Book of ML Metrics
The book every data scientist needs on their desk. Metrics are arguably the most important part of data science work, yet they are rarely taught in courses or university degrees. Even senior data scie...
nannyml.com
Hear me out. Post-deployment data science. The part of data science that focuses on models after they have been deployed. - Checking if the model is delivering value. - Continuously estimating model performance. - Understanding performance issues and fixing them.
Univariate data drift doesn't show the full picture. Take a look at this demo created by my colleague @anopsy.bsky.social There are two univariate distributions (top and right) which remain almost unchanged during the whole process.
Yesterday I forgot to post about our study group meeting 😅 It was an amazing one! @carloscapote.bsky.social walked us through Chapter 5: Pretraining on Unlabeled Data. Next week, we’ll take a short break, but we’ll be back after the holidays to finish Chapters 6 and 7 💪
Santiago Viquez@santiviquez.com · 2y agoToday’s session was a fun one. We started putting everything together and implemented a GPT model. My favorite part of the book is the way Sebastian outlined and structured it to progressively build on previous sections. That alone makes it worth every penny.
I've almost completed the preparations for the session about pretraining. For the moment, I'm pretty happy with the results. Sebastian's book is a source of information and inspiration. Now I've so many ideas about how to inspect an LLM to understand it better. 🎉 github.com/elcapo/llm-f...
llm-from-scratch/chapter-5.ipynb at main · elcapo/llm-from-scratch
Implementation of an LLM from scratch following Sebastian Raschka's book. - elcapo/llm-from-scratch
github.com
F1-score often takes all the credit. But what F1-score doesn't want you to know is that it wouldn't be so popular without its big brother, F-beta. Check out other metrics at: github.com/NannyML/The-...
My notes from Chapter 4: "Implementing a GPT Model from Scratch to Generate Text" www.santiviquez.com/blog/llm-scr...
Notes on "Build a Large Language Model (from scratch)" [WIP]
Collection of notes while reading Sebastian Raschka's book on building LLMs from scratch.
santiviquez.com
Santiago Viquez@santiviquez.com · 2y agoMy notes from Chapter 3 of “Build a Large Language Model (from scratch)” www.santiviquez.com/blog/llm-scr...
800 GitHub stars on The Little Book of ML Metrics 🌟 But even more exciting than the stars is that for the past week, every day I've been waking up to at least two PRs from the community helping me write the book 🔥
Today’s session was a fun one. We started putting everything together and implemented a GPT model. My favorite part of the book is the way Sebastian outlined and structured it to progressively build on previous sections. That alone makes it worth every penny.
Santiago Viquez@santiviquez.com · 2y agoToday's session was a great one. I feel like the intuition behind Q, K, and V matrices in self-attention finally clicked for many of us.
We need a Conda wrapped. You waited 10523 minutes installing PyTorch this year.
Performance metrics are aggregates. Watch out for underperforming segments 👀
There are scenarios where data drift can improve model performance. This happens when the production data moves to regions where the model is more confident in its predictions.
