I'm an avid reader of many different topics however, as a software engineer, I obviously read a fair bit about technical topics. Much of the time I'm reading to learn for application.

I've gone through a number of job application cycles where, I have the skills, but I think the job needed to see "proof". Working internally on projects within large companies often makes you lack prof of work, and you're often working on less than leading edge tech. Circling back to my reading I often read to apply more leading edge things.

Which brings me to this new side quest. I want to take the same learning approach that I used in college (reading, taking, notes, rehashing, and homework) to the books I'm currently reading. This will make the topics 1) stick, and 2) have proof. I don't know if the proof will actually matter to anyone, but it will matter to me.

The first book is "Machine Learning Q and AI" written by Sebastian Raschka. He actually writes a lot on his blog about machine learning and AI. I want to get back into the inference infrastructure space, so he's a good start.

I'll continue on with other books in similar fashion. Sometimes I graze, so there will probably be interleaving.