Training Paradigms
6 topics in AI & Machine Learning
Contrastive Learning
How AI learns what things are like each other — and what they're not — without any labels, creating the representations behind image search and face recognition.
Data Augmentation
How AI systems make do with less data by creating variations of what they have — the training trick that prevented ImageNet models from memorizing training examples.
Few-Shot Learning
How AI learned to learn from just a handful of examples — the technique that lets AI generalize like humans instead of needing millions of training samples.
LoRA Fine-Tuning
How AI companies adapt massive models to specific tasks by training only a tiny fraction of the parameters — the technique making custom AI affordable.
Reinforcement Learning
How AI learns from trial, error, and rewards — the technique that beat the world chess champion, solved protein folding, and is now teaching robots to walk.
Self-Supervised Learning
How AI learned to teach itself from unlabeled data — the technique that let GPT and BERT learn from the entire internet without any human labeling.