Machine Learning for Engineers: Book List
In 2018, I posted a series of introductory, hands-on and more advanced book lists for engineers new to machine learning, to help develop a basic knowledge of machine learning fundamentals. This post provides an updated set of book recommendations reflecting changes since then, and an improved grouping for the books into mathematics, machine learning, data science, and programming tools.
Some books were read: 2019
Here are some notes on a few books I enjoyed and learned from in 2019, not all of which are books published that year.
Reasoning about Leverage in Engineering Organisations
Leverage is an important concept for an engineering organisation. When we are debating whether to standardise and on what, which technology tools and stacks to use, whether to add new technology, replace an old system with a new one, which programming languages to use and how many, under the surface we are ultimately talking about or around, the topic of leverage of technology.
Principles
A set of principles that have been meaningful and helpful to me. Nothing particularly originally, just that this set of approaches have worked as I’ve gone along. It took me a while to identify some of these, and in more than one (!) place, some time to figure out the would work and how to apply them 😀
MacBook Pro 2018 & Software Essentials
After 3 years, it was time to retire the old Macbook Pro 2015 and replace it with a 2018 touchbar model. Also some thoughts on software essentials since the last time I went through things, back in 2010 for a W500 Thinkpad, the last non-Mac laptop I used before switching.
Paper Reading 📄
A list of technical papers and memos that I want to read or re-read. I’d love to get recommendations on other papers especially from the last decade, that seem important or in some way foundational.
Thoughts for 2019
It's always interesting to look at the year ahead in technology, and so, some thoughts about 2019! These are less predictions, more extrapolations of what's already happening.