Neural networks have revolutionized many fields, from picture recognition to pure language processing. However for novices, the high-level libraries and frameworks can typically obscure the core mechanics of how these algorithms work. On this lengthy and technical weblog article, we’ll construct a totally functioning neural community for the XOR drawback from scratch — within the Go programming language.
We are going to discover the arithmetic behind feedforward networks, stroll by the code line by line, focus on use circumstances for such a community, define the advantages of implementing it your self, and eventually study the outcomes. By the tip, you should have a deep appreciation for each the conceptual underpinnings and sensible implementation particulars of primary neural networks in Go.
Creating a neural community from scratch can really feel like climbing a steep mountain: you have to perceive the arithmetic, the information movement, the replace mechanisms, and the code construction. Nonetheless, when you attain the highest of that mountain, you acquire unparalleled perception into how these fascinating fashions really perform. In most tutorials, you would possibly see high-level frameworks like TensorFlow, PyTorch, or Keras. Whereas they’re wonderful for sensible purposes, they usually summary away important particulars — resembling…
