Brandon Rohrer

I shut down by online courses at End-to-End Machine Learning 18 months ago. Wrote a blog post and everything. But the platform I was using hasn't bothered to take them down. So they're just sitting there, all for free. Prego. end-to-end-machine-learning.teachable.com

Homepage | End to End Machine Learning end-to-end-machine-learning.teachable.com

3. xgboost_regression: optimizes an XGBoost regression model across a wide variety of hyperparameters and options codeberg.org/brohrer/reds... 4. xgboost_multiclass: same as the previous example, but with a multiclass classification problem codeberg.org/brohrer/reds...

Cookie monster! codeberg.org

3. xgboost_regression: optimizes an XGBoost regression model across a wide variety of hyperparameters and options codeberg.org/brohrer/reds... 4. xgboost_multiclass: same as the previous example, but with a multiclass classification problem codeberg.org/brohrer/reds...

Cookie monster! codeberg.org

As of right now, there are 4 examples, each in their own subdirectory: 1. sinc: optimizes a 2D variant of the sinc function, sin(x)/x codeberg.org/brohrer/reds... 2. perf: tests the execution time bottleneck in redsho and helps to streamline it codeberg.org/brohrer/reds...

Cookie monster! codeberg.org

As of right now, there are 4 examples, each in their own subdirectory: 1. sinc: optimizes a 2D variant of the sinc function, sin(x)/x codeberg.org/brohrer/reds... 2. perf: tests the execution time bottleneck in redsho and helps to streamline it codeberg.org/brohrer/reds...

Cookie monster! codeberg.org

I recently published the redsho package for hyperparameter optimization. More than anything, I want it to be useful. I find it helpful to start with a working example. To this end I created a second repo called redsho-examples that walks through how to do it. codeberg.org/broh…

redsho-examples codeberg.org

Hey US eng friends, if you want to give Patreon a shot: Staff SWE, Data Infrastructure jobs.ashbyhq.com/patreon/e8f0... Senior MLE, Infrastructure (on my team) jobs.ashbyhq.com/patreon/722d... Staff SWE, Backend Platform jobs.ashbyhq.com/patreon/c277...

Senior Machine Learning Engineer, Infrastructure jobs.ashbyhq.com

If you’re into such things, I’m going to be chatting with Josh Starmer and Luis Serrano (two of the best ML educators I know) in a YouTube livestream, answering viewer questions, at 11:00am EDT Thursday (tomorrow/today). 8am PDT, 3pm UTC, 5pm CEST m.youtube.com/live/Yx-ZkLf...

TRIPLE BAM!!! With Josh, Luis and special guest Brandon Rohrer m.youtube.com

Artisanal Language Models take a step closer to being able to do proofreading. Trained and hosted on an old Mac. These are small but mighty. Kind of. Aspirationally mighty, at least. brandonrohrer.org/alms_data.html

brandonrohrer.org

the biggest difference I've noticed between data scientists and machine learning engineers is that MLEs will do absolutely anything to avoid looking at their data

The earliest Large Language Models were Markov models (a.k.a. Markov chains). They are like LLMs with a context window of 1 token. As you might guess, they are OK at some things but not awesome.