andersbll/deeppy
Deep learning in Python observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4367
- days_rel: n/a
- days_push: 2074
- n_releases_24m: 0
Adoption not part of the score
1370 stars · 297 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
DeepPy is a Pythonic deep learning framework built on top of NumPy, with optional CUDA acceleration via the cudarray library. It provides a lightweight, educational approach to defining and training neural networks in Python.
Use cases
- train neural networks in python
- learn how deep learning frameworks work internally
- build neural networks on top of numpy
- experiment with gpu-accelerated numpy arrays
- implement a small deep learning model from scratch
When to choose
- you want a minimal, readable codebase for learning deep learning internals
- you need a simple NumPy-based neural network framework for experimentation
When to avoid
- you need a production-ready framework with active maintenance and community support
- you require modern features like automatic differentiation graphs, distributed training, or transformer support
- you need up-to-date CUDA hardware support
Facets
framework · maturity abandoned
deep-learning machine-learning gpu-computing deep-learning machine-learning python numpy neural-networks cuda gpu
1 source
- readme: https://github.com/andersbll/deeppy · fetched 2026-08-28 · 74fb623c150c
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| andersbll/deeppy | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="andersbll/deeppy")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem