Machine Learning With Go, Second Edition, will begin by helping you gain an understanding of how to gather, organize, and parse real-world data from a variety of sources. The book also provides absolute coverage in developing groundbreaking machine learning pipelines including predictive models, data visualizations, and statistical techniques. Up next, you will learn the thorough utilization of Golang libraries including golearn, gorgonia, gosl, hector, and mat You will discover the various TensorFlow capabilities, along with building simple neural networks and integrating them into machine learning models.
You will also gain hands-on experience implementing essential machine learning techniques such as regression, classification, and clustering with the relevant Go packages. Furthermore, you will deep dive into the various Go tools that help you build deep neural networks. Lastly, you will become well versed with best practices for machine learning model tuning and optimization. By the end of the Machine Learning With Go, 2nd Edition book, you will have a solid machine learning mindset and a powerful Go toolkit of techniques, packages, and example implementations.
They argue that the Second Machine Age involves the automation of a lot of cognitive tasks that make humans and software-driven machines substitutes, rather than complements. They contrast this with what they call the 'First Machine Age', or Industrial Revolution, which helped make labor and machines complementary. Some examples that the book cites include 'software that grades students' essays more objectively, consistently and quickly than humans' and 'news articles on Forbes.
The authors summarize the contents of their book's 15 chapters [3] on pages 11 and 12 of the book itself. The book is divided into three sections: Chapters 1 through 6 describe 'the fundamental characteristics of the second machine age,' based on many examples of modern use of technology. Chapters 7 through 11 describe economic impacts of technology in terms of two concepts the authors call 'bounty' and 'spread.
Whatever we select for our library has to excel in one or the other of these two core criteria:. We rate each piece of content on a scale of 1—10 with regard to these two core criteria. Our rating helps you sort the titles on your reading list from solid 5 to brilliant Here's what the ratings mean:.
Often an instant classic and must-read for everyone. For instance, it may offer decent advice in some areas while being repetitive or unremarkable in others. While the rating tells you how good a book is according to our two core criteria, it says nothing about its particular defining features. Therefore, we use a set of 20 qualities to characterize each book by its strengths:.
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