Hacker News
- Lightly – A Python library for self-supervised learning https://github.com/lightly-ai/lightly 3 comments
Linking pages
- GitHub - josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software. https://github.com/josephmisiti/awesome-machine-learning 58 comments
- GitHub - qdrant/awesome-metric-learning: 😎 A curated list of awesome practical Metric Learning and its applications https://github.com/qdrant/awesome-metric-learning 20 comments
- GitHub - mikeroyal/Neuromorphic-Computing-Guide: Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures. https://github.com/mikeroyal/Neuromorphic-Computing-Guide 7 comments
- GitHub - mikeroyal/Machine-Learning-Guide: Machine learning Guide. Learn all about Machine Learning Tools, Libraries, Frameworks, and Training Models. https://github.com/mikeroyal/Machine-Learning-Guide 2 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
- GitHub - r0f1/datascience: Curated list of Python resources for data science. https://github.com/r0f1/datascience 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
- GitHub - IgorSusmelj/pytorch-styleguide: An unofficial styleguide and best practices summary for PyTorch https://github.com/IgorSusmelj/pytorch-styleguide 0 comments
- GitHub - ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities. https://github.com/ChristosChristofidis/awesome-deep-learning 0 comments
- Machine Learning Toolbox https://amitness.com/toolbox/ 0 comments
- GitHub - bharathgs/Awesome-pytorch-list: A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc. https://github.com/bharathgs/Awesome-pytorch-list 0 comments
- GitHub - IgorSusmelj/simsiam-cifar10: Code to train the SimSiam model on cifar10 using PyTorch https://github.com/IgorSusmelj/simsiam-cifar10 0 comments
- GitHub - lightly-ai/NvidiaTLTActiveLearning: Tutorial on active learning with the Nvidia Transfer Learning Toolkit (TLT). https://github.com/lightly-ai/NvidiaTLTActiveLearning 0 comments
- GitHub - Renumics/awesome-open-data-centric-ai: Curated list of open source tooling for data-centric AI on unstructured data. https://github.com/Renumics/awesome-open-data-centric-ai 0 comments
Linked pages
- PyTorch http://pytorch.org/ 100 comments
- [2304.12210] A Cookbook of Self-Supervised Learning http://arxiv.org/abs/2304.12210 27 comments
- [2103.03230] Barlow Twins: Self-Supervised Learning via Redundancy Reduction https://arxiv.org/abs/2103.03230 22 comments
- [2002.05709] A Simple Framework for Contrastive Learning of Visual Representations https://arxiv.org/abs/2002.05709 18 comments
- [2305.15614] Reverse Engineering Self-Supervised Learning https://arxiv.org/abs/2305.15614 16 comments
- [2110.06848] Decoupled Contrastive Learning https://arxiv.org/abs/2110.06848 9 comments
- GitHub - psf/black: The uncompromising Python code formatter https://github.com/psf/black 8 comments
- [2111.06377] Masked Autoencoders Are Scalable Vision Learners https://arxiv.org/abs/2111.06377 5 comments
- [2006.07733] Bootstrap your own latent: A new approach to self-supervised Learning https://arxiv.org/abs/2006.07733 5 comments
- [2104.14294] Emerging Properties in Self-Supervised Vision Transformers https://arxiv.org/abs/2104.14294 3 comments
- Black 23.11.0 documentation https://black.readthedocs.io/en/stable/ 3 comments
- PyTorch Lightning https://pytorchlightning.ai/ 0 comments
- [2105.04906] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning https://arxiv.org/abs/2105.04906 0 comments
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