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:
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: