Linking pages
- GitHub - AsuharietYgvar/AppleNeuralHash2ONNX: Convert Apple NeuralHash model for CSAM Detection to ONNX. https://github.com/AsuharietYgvar/AppleNeuralHash2ONNX 178 comments
- Semantic Import Versioning in the wild : ezyang’s blog http://blog.ezyang.com/2018/02/semantic-import-versioning-in-the-wild/ 10 comments
- Snips Open Sources Tract. The story of how we run neural networks… | by Mathieu Poumeyrol | Snips Blog | Medium https://medium.com/snips-ai/snips-open-sources-tract-cdc50f437ef2 8 comments
- Debug ONNX GPU Performance. What to do when your model is slower… | by David Mezzetti | NeuML | Nov, 2022 | Medium https://medium.com/neuml/debug-onnx-gpu-performance-c9290fe07459 2 comments
- Blockchain GPUs Unchained: Running Neural Networks Without Hurting Mining Hash Rate | by Activeloop | Medium https://medium.com/@SnarkAI/blockchain-gpus-unchained-running-neural-networks-without-hurting-mining-hash-rate-38a88728a1c9 1 comment
- GitHub - 1duo/awesome-ai-infrastructures: Infrastructures™ for Machine Learning Training/Inference in Production. https://github.com/1duo/awesome-ai-infrastructures 1 comment
- LLMs Everywhere: Running 70B models in browsers and iPhones using MLC — with Tianqi Chen of CMU / OctoML https://www.latent.space/p/llms-everywhere#details 1 comment
- GitHub - alibaba/MNN: MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba https://github.com/alibaba/MNN 0 comments
- Deploying Keras Deep Learning Models with Java | by Ben Weber | Towards Data Science https://towardsdatascience.com/deploying-keras-deep-learning-models-with-java-62d80464f34a 0 comments
- GitHub - AngeloKai/ml-for-web: Explainer for the need of a new set of APIs for ML on the Web https://github.com/AngeloKai/ml-for-web 0 comments
- GitHub - EthicalML/awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning https://github.com/EthicalML/awesome-production-machine-learning 0 comments
- Microsoft and Facebook create open ecosystem for AI model interoperability | Azure Blog and Updates | Microsoft Azure https://www.microsoft.com/en-us/cognitive-toolkit/blog/2017/09/microsoft-facebook-create-open-ecosystem-ai-model-interoperability/ 0 comments
- Facebook and Microsoft join forces to make life easier for AI developers https://www.siliconrepublic.com/machines/facebook-microsoft-ai-collaboration 0 comments
- GitHub - ml-tooling/best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. https://github.com/ml-tooling/best-of-ml-python 0 comments
- Distributed Policy Optimizers for Scalable and Reproducible Deep RL - RISE Lab https://rise.cs.berkeley.edu/blog/distributed-policy-optimizers-for-scalable-and-reproducible-deep-rl/ 0 comments
- Using ONNX to Transfer Machine Learning Models from PyTorch to Caffe2 and Mobile | by Derrick Mwiti | Heartbeat https://heartbeat.fritz.ai/transferring-machine-learning-models-from-pytorch-to-caffe2-and-mobile-using-onnx-10eb266eaacb 0 comments
- GitHub - EmergentOrder/onnx-scala: An ONNX (Open Neural Network eXchange) API and backend for typeful, functional deep learning in Scala 3 https://github.com/EmergentOrder/onnx-scala 0 comments
- GitHub - mbadry1/Top-Deep-Learning: Top 200 deep learning Github repositories sorted by the number of stars. https://github.com/mbadry1/Top-Deep-Learning 0 comments
- Announcing ONNX Support for Apache MXNet | AWS Machine Learning Blog https://aws.amazon.com/blogs/ai/announcing-onnx-support-for-apache-mxnet/ 0 comments
- GitHub - sonos/tract: Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference https://github.com/snipsco/tract 0 comments
Linked pages
- GitHub - microsoft/vcpkg: C++ Library Manager for Windows, Linux, and MacOS https://github.com/microsoft/vcpkg 50 comments
- GitHub - onnx/models: A collection of pre-trained, state-of-the-art models in the ONNX format https://github.com/onnx/models 3 comments
- ONNX | Home https://onnx.ai/ 1 comment
- conda-forge | community driven packaging for conda https://conda-forge.org/ 0 comments
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