This section will take a look at the ML ecosystem in Go, first discussing the essentials we want from a library, and then assessing each of the main Go ML libraries in turn.
Go's ML ecosystem has historically been quite limited. The language was introduced in 2009, well before the DL revolution that has brought many new programmers into the fold. You might assume that Go has seen the growth in libraries and tools that other languages have. History, instead, determined that many of the higher-level APIs for the mathematical operations that underpin our networks have appeared as Python libraries (or have complete Python bindings). There are numerous well-known examples of this, including PyTorch, Keras, TensorFlow, Theano, and Caffe (you get the idea).
Unfortunately, these libraries have either zero or incomplete bindings for Go. For example, TensorFlow...
