This interesting book covers the latest issues in big data methodologies, with relevant examples to contextualize the content. The examples and discussion are very informative and full of details to guide the reader. The target audience includes those interested in the software theoretical aspects of big data and those interested in hardware issues. The authors present balanced coverage of the topics.

The most interesting part of the book for me was chapter 8 on deep learning, which reviews field-programmable gate array (FPGA)-based hardware accelerators and single and scalable multi-FPGA systems. Details on the latest advances in neural-network accelerators are also of interest. As an introduction, the machine learning concepts seem to be relevant to early-career research and general postgraduate work.

I would recommend this book for early-career research in deep learning, as well as undergraduate and postgraduate modules covering big data--possibly as an extra study guide if not the main textbook, depending on the context of the module learning outcomes.