Self-Supervised & Few-Shot Learning in AI: Tackling New Tasks with Minimal Data
Small data does not have to mean small results. Self supervised and few shot learning let you build models that learn from the world, not labels. This book shows you how to pretrain strong representations with self supervised objectives, then adapt them to new tasks with only a handful of examples.
You will learn how contrastive learning, masked modeling, and cross modal pretraining shape features that transfer. You will fine tune with techniques like prototypical classifiers, meta learning, prompt tuning, and lightweight adapters so you can move fast without massive compute. The book walks through end to end projects in Python with PyTorch and Hugging Face for vision, text, and audio. You will handle class imbalance, distribution shift, and data leakage, and you will measure what matters with calibration, AUROC and AUPRC, few shot evaluation protocols, and robust validation splits. Clear recipes for augmentation, synthetic data, and active learning help you squeeze the most out of every example.
This is for data scientists, ML engineers, and researchers who need working systems, not just theory. By the last chapter you will have models that start from self supervised checkpoints, adapt in minutes, and ship with confidence.
If you want practical methods that cut labeling costs and improve generalization, buy this book now and start building smarter models with less data.
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