Machine Learning for Engineers: Book List 2024
At the end of 2019, I posted a book list for engineers new to machine learning, to help develop basic knowledge of the fundamentals, organised into four groups: Machine Learning & Algorithms, Tools & Frameworks, Data Science & Analysis, and Companion Mathematics. This post provides an updated book list using the same groups, which have held up well. At the time the list covered 26 books, this iteration is more concise, covering 11 books. This time around 3 are new, and 3 have seen updated editions in the interim.
Read MoreSome Books Were Read: 2021
Here are some notes on a few books I enjoyed and learned from in 2021, not all of which are books published that year.
Read MoreWhat I’ve been reading
Some notes on some books.
Read MoreMachine 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.
Read MoreMachine Learning For Engineers: Reading List 1
This is the first of a series of book reading lists written from the point of view of a software engineer who wants to develop a basic knowledge of machine learning fundamentals. In this part we'll look some introductory books and some background books for machine learning mathematics.
Read MoreSome Books Were Read: 2017
A nice thing about 2017 was getting to read more books than previous years, with the aim of working my way back to an inveterate reader. Here are some notes on a few I enjoyed over the year.
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