The wait is over. After four years of hard work, the release of the Mathematics of Machine Learning book is right around the corner!
First, there were a couple of Twitter threads explaining formulas like the mean-squared error or the expected value. (Here’s a snippet of history, if you are curious.) You loved it, and I realized that, unknowingly, I’m writing a book.
So, exactly four years ago. I began to work on it. Thousands of work hours later, it’s finally here, and I’m offering you
a free preview,
and a discount code for the full book!
If you are interested, sign up here to get all the above, and a notification when the book is officially available.
To celebrate the book’s release, there’ll also be a LinkedIn live event on May 28th, where you can meet me and ask questions. You can join with the link here or watch live on YouTube. (Video embed below.)
This’ll be my first live ever, so I’m feeling a mix of excitement and nervousness.
Your support made this book possible, and the amount of love I’ve received from you in the past couple of years is almost unbelievable. I’m just a simple guy sitting at home, writing about mathematics.
Thank you, and see you soon!
Cheers,
Tivadar
I’m very much looking forward to getting my copy! I also have a wild idea - would you consider a live class on this? It would be for professionals working in the industry for a while and can really use a refresher AND a clarity on ML math. Maybe a quarter long course? I’d love for someone like to teach us.
I‘m wondering: what advantages does your book have over Deisenroth, Faisal, and Ong’s ‘Mathematics for Machine Learning’? Are there different emphases, different targeted readers?
Btw, big fan of your Substack posts and your style of explanation.