Summary: In this post I talk about the basics of computations, then I go on to show parallels between Go programs and mathematical equations. Lastly I will attempt to persuade you that there is a point in using Go for doing deep learning.
Welcome to the first part of many about writing deep learning algorithms in Go. The goal of this series is to go from having no knowledge at all to implementing some of the latest developments in this area.
Deep learning is not new. In fact the idea of deep learning was spawned in the early 1980s. What’s changed since then is our computers - they have gotten much much more powerful. In this blog post we’ll start with something familiar, and edge towards building a conceptual model of deep learning. We won’t define deep learning for the first few posts, so don’t worry so much about the term.
There are a few terms of clarification to be made before we begin proper. In this series, the word “graph” refers to the concept of graph as used in graph theory. For the other kind of “graph” which is usually used for data visualization, I’ll use the term “chart”.
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