# sudharsan13296/Hands-On-Meta-Learning-With-Python

Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow

Repository: https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python
Canonical: https://ross.abutalabs.com/products/hands-on-meta-learning-with-python
Homepage: https://www.amazon.com/Hands-Meta-Learning-Python-algorithms-ebook/dp/B07KJJHYKF/ref=sr_1_1?ie=UTF8&qid=1543222179&sr=8-1&keywords=meta+learning+hands+on
Language: Jupyter Notebook
License Family: other
Topics: metalearning, maml, reptile, meta-sgd, tensorflow, ntm, mann, one-shot-learning, few-shot-learning, matching-networks, siamese-network, prototypical-networks, relation-network, deep-meta-learning, meta-imitation-learning, keras, shot-learning, prototypical-network, reinforcement-learning, zero-shot-learning
Last push: 2021-09-19T05:40:56+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2824, "days_push": 1809, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1228, forks 359 (observed 2026-08-28T04:04:03.506372+00:00)

## What it is
A Jupyter Notebook companion repository for the book 'Hands-On Meta Learning with Python', implementing meta-learning algorithms like MAML, Reptile, Meta-SGD, and one-shot learning networks in TensorFlow and Keras. It serves as educational material for learning-to-learn techniques in deep learning.

## Use cases
- learn meta learning algorithms with python
- implement MAML from scratch
- understand one-shot and few-shot learning
- study prototypical networks and siamese networks
- hands-on tutorial for learning to learn
- implement reptile and meta-SGD in tensorflow

## When to choose
- you want to learn meta-learning concepts through runnable code
- you are reading the companion book and want implementations
- you need educational notebooks on few-shot learning algorithms

## When to avoid
- you need a production-ready meta-learning library
- you require an actively maintained project with a license
- you want the latest research implementations beyond the book's scope

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, reinforcement-learning
- domain: machine-learning, deep-learning, tutorials
- platform: python
- tags: meta-learning, few-shot-learning, one-shot-learning, maml, reptile, tensorflow, keras, jupyter-notebook, book-companion

## Member repositories
- sudharsan13296/Hands-On-Meta-Learning-With-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:03.506372+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:15:12.637830+00:00, confidence not recorded.
  - readme: https://github.com/sudharsan13296/Hands-On-Meta-Learning-With-Python (fetched 2026-08-28T04:04:03.506372+00:00, sha a282965ab8d1)
  - homepage: https://www.amazon.com/Hands-Meta-Learning-Python-algorithms-ebook/dp/B07KJJHYKF/ref=sr_1_1?ie=UTF8&qid=1543222179&sr=8-1&keywords=meta+learning+hands+on (fetched 2026-08-29T12:23:08.017582+00:00, sha 6d1d7ac1af17)
- Data as of 2026-08-30T08:39:29.467469+00:00.