Finding the right way to deliver a complex message in short form is the hallmark of someone in command of their craft. Often times, when topics such as machine learning and artificial intelligence are introduced for the first time, they seem like a foreign language – with an insurmountable volume of content to digest before getting anywhere. After grasping the basics, moving towards execution, mastery, and deployment of AI/ML presents the neophyte with tomes of textbooks that feel like they may become irrelevant by the time they are able to finish them.
This is the backdrop for Andriy Burkov’s “The Hundred-Page Machine Learning Book”. Two interesting facts:
This is not actually a hundred-page book. It’s 141 pages (but worth the deviation).
In a short amount of time, the author exposes you to fundamentals, best practices, advanced solutions, and more within machine learning and deep learning. Yes, there is math. Yes, there are code snippets. But also, there are pictures, detailed narratives, and recommended additional resources that help make sure you actually understand things while moving at a steady pace.
This isn’t a 30,000 ft view that you might get from introductory or thought-leadership level books, nor is it a ground-level technical field resource supported by a Github repository. It’s a bridge in the middle and helps the reader accomplish a few helpful things, such as:
Exposing you to a breadth of topics so you have a common vocabulary when making decisions about AI or working with practitioners
Showing you where you may have knowledge gaps – if you are struggling with a topic that is only covered for a few pages, it might be time to explore more
Providing a quick reference book for your tool belt if you need to recall information about AI in real-time but don’t need or want the full context (or if the internet is down…)