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Summary
Practical tutorial building a feedforward neural network from scratch in Go, starting with the intuition of a “guessing game” (minimize prediction error by adjusting parameters) and building up to full backpropagation. Explains why traditional algorithms fail for pattern-recognition tasks and how ANNs learn through iterative error minimization.
從零開始構建前饋神經網絡的 Go 語言實踐教程，從「猜謎遊戲」的直覺開始（通過調整參數最小化預測誤差），逐步建立完整的反向傳播。解釋了為何傳統算法對模式識別任務失效，以及 ANN 如何通過迭代誤差最小化學習。
Key Points
- Traditional programs excel at precise algorithms but struggle with ill-defined pattern recognition
- ANN core intuition: a “predictor” with configurable parameters; minimize error by adjusting parameters through repeated trials
- Formula
o = i × cas minimal example: given input 10, output 26, find c using error feedback - Mental model: inputs → weighted connections → neurons → activations → outputs; learn by adjusting weights to reduce error
- Implementation language: Go (2018 article; pre-deep-learning-frameworks era for Go)
Insights
The “guessing game” framing is an effective pedagogical approach — it demystifies neural networks as “magic” by reducing them to the familiar concept of iteratively adjusting a configurable parameter to minimize error. Building from scratch in Go (rather than Python/TensorFlow) forces engagement with the underlying math. The contrast between “programs as precise instructions” and “ANNs as learned approximations” is a useful conceptual dividing line that helps explain when to use each approach.
Connections
Raw Excerpt
What computer programs are not so good at doing, are tasks that are not so well defined, and doesn’t follow precise patterns. So how can we use computers to do such tasks? We try to create a model that we can use to compare inputs with, through a trial and error process.