Hands-On One-shot Learning with Python

How can a model learn a useful concept from just one example—or a small handful? Hands-On One-shot Learning with Python explores that challenge through the ideas and architectures behind one-shot and few-shot learning, pairing conceptual explanations with Python implementations.

Shruti Jadon and Ankush Garg guide readers from the foundations of the field to several distinct approaches. The emphasis is not on one all-purpose recipe, but on understanding how different methods tackle learning when examples are scarce.

Explore several routes to learning from few examples

The book organizes its main approaches around metric-based, model-based, optimization-based, and generative modeling methods. Along the way, it introduces k-nearest neighbors, Siamese and matching networks, Neural Turing Machines, memory-augmented networks, model-agnostic meta-learning, and Bayesian program learning. Seeing these approaches side by side helps readers build a broader picture of the design choices involved in one-shot learning.

Connect the ideas to Python practice

Practical exercises bring the discussion into code, including implementations using PyTorch and scikit-learn. The examples draw on datasets such as MNIST and Omniglot, giving readers a concrete setting in which to follow the methods being discussed.

Look beyond the core techniques

The final chapters consider few-shot object detection and image segmentation, alongside neighboring fields such as semi-supervised learning, imbalanced learning, meta-learning, and transfer learning. These connections help place one-shot learning within a wider machine-learning landscape.

Who may find it useful?

The authors present the book as an introductory resource for AI researchers and readers with machine-learning or deep-learning experience. It will suit readers who want to understand how models can work with limited training examples and compare a range of approaches through practical implementations.

For a focused tour of one-shot learning—from core concepts to working examples—this book offers a structured place to begin.