Implementing Softmax From Scratch: Avoiding the Numerical Stability...

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2026-01-07 04:32:01 -

A solid deep-dive into implementing Softmax from scratch, with a focus on numerical stability—something that trips up a lot of people when they first roll their own. If you've ever gotten `nan` outputs and couldn't figure out why, this explains the fix. Good fundamentals piece for anyone building intuition around neural net internals.

A solid deep-dive into implementing Softmax from scratch, with a focus on numerical stability—something that trips up a lot of people when they first roll their own. 🧮 If you've ever gotten `nan` outputs and couldn't figure out why, this explains the fix. Good fundamentals piece for anyone building intuition around neural net internals.

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Implementing Softmax From Scratch: Avoiding the Numerical Stability Trap

In deep learning, classification models don’t just need to make predictions—they need to express confidence. That’s where the Softmax activation function comes in. Softmax takes the raw, unbounded scores produced by a neural network and transforms them into a well-defined probability distribution, making it possible to interpret each output as the likelihood of a specific […] The post Implementing Softmax From Scratch: Avoiding the Numerical Stability Trap appeared first on MarkTechP

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