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.

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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.

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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.

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On Rust

Back in 2013, I started a series of posts on programming languages I found interesting. One of the languages I wanted to write about at that time was Rust. As often happens, life got in the way, and it’s only now that I’m coming round to a long overdue post. This is one of a series of posts on programming languages and you can read more about that here.

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