Abstract Algorithms

neural networks

3 articles across 3 sub-topics

Transfer Learning Explained: Standing on the Shoulders of Pretrained Models

TLDR: You don't need millions of labeled images or months of GPU time to build a great model. Transfer learning lets you borrow a pretrained network's hard-won feature detectors, plug in a new output head, and fine-tune on your small dataset — often ...

Apr 18, 2026•26 min read

Attention-mechanism(1)

Attention Mechanism Explained: How Transformers Learn to Focus

TLDR: Attention lets every token in a sequence ask "what else is relevant to me?" — dynamically weighting relationships across all positions simultaneously. It replaced the fixed-size hidden-state bottleneck of RNNs and is the engine behind every GPT...

Apr 18, 2026•24 min read

Ai(1)

What are Logits in Machine Learning and Why They Matter

TLDR: Logits are the raw, unnormalized scores produced by the final layer of a neural network — before any probability transformation. Softmax converts them to probabilities. Temperature scales them before Softmax to control output randomness. 📖 T...

Mar 9, 2026•11 min read