Tag: ml-engineering
All the articles with the tag "ml-engineering".
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Bias, Variance, and the Tradeoff Every Model Faces
Why models fail in two opposite ways — being too rigid or too sensitive — and how to find the sweet spot between them.
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Dropout and Overfitting: Teaching a Network Not to Cheat
What overfitting is, why it happens, and how dropout stops a network from memorising the training data.
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Normalization: BatchNorm, LayerNorm, and Why Transformers Need a Different One
Why activations drift as they pass through deep networks, and how BatchNorm and LayerNorm fix it in different ways.
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Optimizers: SGD, Momentum, Adam, and AdamW
Why plain gradient descent isn't enough, and how SGD, momentum, Adam, and AdamW each fix a problem the previous one had.