The Hundred Page Machine Learning Book

I couldn’t have asked for a denser review of such a technical and diverse domain. It’d been more a year since I graduated (majored in computer science and minored in artificial intelligence) and so I decided to brush up on some basics and consolidate what I’d studying since the past ~4 years. I also picked up the book to systematically populate my org-roam buffer and have my root node rooted here in the braindump.

Practical Natural Language Processing : Chapters 1-5

I took this up when I had to setup a bunch of NLP pipelines for work and this does stand true to its name - it is a quick and practical index into approaches, introductory theory and useful libraries for the same. I don’t like reading text books at a stretch due to several reasons:- “cross a bridge, when you get to it” is something that has stood the test of time for me when it comes to reading practical books.

Common Lisp - A Gentle Introduction to Symbolic Computation

Given I’ve passed through SICP once, quickly grasping common lisp to build stuff and explore the traditional and industrial aspects of lisp (I know clojure exists but traditional…) was my next objective: with decent speed and only solving the somewhat involved exercises, it took me two weeks to complete this book. The exercises aren’t meant to be a challenge but to adapt to the environment and the topics introduced. The book does not explore concepts with depth (CLOS, macros, etc…) but that shouldn’t be the objective of an introduction anyway.