ilkarman/DeepLearningFrameworks resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1651 stars · 353 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks implementing the same neural network tasks (CNN on CIFAR-10, RNN, etc.) across many deep learning frameworks to serve as a 'Rosetta Stone' for data scientists. It also provides GPU training-time benchmarks for comparing framework performance.

Use cases

• compare deep learning framework training speed on GPU
• translate my PyTorch knowledge to TensorFlow or MXNet
• learn how to implement a CNN in multiple frameworks
• verify performance of my deep learning installation
• find equivalent API calls across deep learning frameworks
• benchmark Keras vs PyTorch vs TensorFlow on CIFAR-10

When to choose

• you want side-by-side, same-task code examples across many DL frameworks
• you need GPU performance benchmarks to pick a framework
• you are migrating expertise from one framework to another

When to avoid

• you need up-to-date examples for modern framework versions (many frameworks like CNTK, Caffe2, Theano are deprecated)
• you want production-ready training pipelines rather than comparison demos
• you need benchmarks on the latest GPU hardware or CUDA versions

Facets

learning-resource · maturity maintenance

deep-learning benchmarking machine-learning deep-learning machine-learning data-science tutorials python jvm cloud jupyter-notebooks framework-comparison rosetta-stone cifar-10 vgg gpu-benchmarks keras pytorch tensorflow mxnet cntk caffe2 chainer julia r gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ilkarman/DeepLearningFrameworks")