Posts
All the articles I've posted.
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CNN, RNN, LSTM: The Road to Transformers
The architectures that came before Transformers, the limits they hit, and why each new design was needed to fix the previous one.
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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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Transformer Architecture & Key Design Decisions
A deep dive into the transformer architecture, why decoder-only models won, and the key design decisions — RoPE, GQA, Flash Attention, MoE — that define every modern LLM.