I strongly believe machine learning methods should not be tied to programming languages. If tomorrow a new language comes out that offers better performance than Go, while keeping the developer friendliness of Go, I'd move to that language in a heartbeat. I wouldn't have to be worried about having to relearn new machine learning methods. I already know them. I can simply rewrite them in that new language. As such, my recommendations would be language-agnostic.

If you want to learn more about machine learning algorithms, I recommend Christopher Bishop's, Pattern Recognition and Machine Learning. It's a slightly older book, but you'll be surprised at how many new developments in machine learning have their roots in that tome.

If you want to learn more about deep learning, I recommend Ian Goodfellow and Yoshua Bengio's, Deep Learning. It's a new book—it's extremely theoretical, with no code, but the insights gained will be priceless.

If you want to learn more about deep learning using Go and Gorgonia, there is an upcoming book by Darrell Chua and Gareth Seneque, published by Packt. It covers a wide range of deep-learning-related topics.

If you want to learn more about data science and machine learning in Go, I also recommend Daniel Whitenack's, Machine Learning with Go. It's one of the first books on machine learning in Go, and to this day, it still stands as an excellent resource.

If you want to learn more about Go, I highly recommend The Go Programming Language, by Alan Donovan and Brian Kernighan. Kernighan is the K in the famous K&R book on C. Here, he performs a similar feat.