langchain-ai / langchain
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🦜🔗 Build context-aware reasoning applications
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Multi-Joint dynamics with Contact. A general purpose physics simulator.
Flax is a neural network library for JAX that is designed for flexibility.
Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
📚 Jupyter notebook tutorials for OpenVINO™
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Learn OpenCV : C++ and Python Examples
A multi-voice TTS system trained with an emphasis on quality
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
cube studio开源云原生一站式机器学习/深度学习/大模型AI平台,支持sso登录,多租户,大数据平台对接,notebook在线开发,拖拉拽任务流pipeline编排,多机多卡分布式训练,超参搜索,推理服务VGPU,边缘计算,serverless,标注平台,自动化标注,数据集管理,大模型微调,vllm大模型推理,llmops,私有知识库,AI模型应用商店,支持模型一键开发/推理/微调,支持国产cpu/gpu/npu芯片,支持RDMA,支持pytorch/tf/mxnet/deepspeed/paddle/colossalai/horovod/spark/ray/volcano分布式
Jupyter Interactive Notebook
Using Low-rank adaptation to quickly fine-tune diffusion models.
T81-558: Keras - Applications of Deep Neural Networks @washington University in St. Louis
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Helium Improvement Proposals
MONAI Tutorials