#python #automl #computer_vision #data_science #deep_learning #distributed_computing #ensemble_learning #gluon #image_classification #machine_learning #mxnet #natural_language_processing #object_detection #pytorch #structured_data #transfer_learning
https://github.com/awslabs/autogluon
https://github.com/awslabs/autogluon
GitHub
GitHub - autogluon/autogluon: Fast and Accurate ML in 3 Lines of Code
Fast and Accurate ML in 3 Lines of Code. Contribute to autogluon/autogluon development by creating an account on GitHub.
#javascript #ai #caffe #caffe2 #coreml #darknet #deep_learning #deeplearning #keras #machine_learning #machinelearning #ml #mxnet #neural_network #onnx #paddle #pytorch #tensorflow #tensorflow_lite #torch #visualizer
https://github.com/lutzroeder/netron
https://github.com/lutzroeder/netron
GitHub
GitHub - lutzroeder/netron: Visualizer for neural network, deep learning and machine learning models
Visualizer for neural network, deep learning and machine learning models - lutzroeder/netron
#python #deep_learning #forecasting #machine_learning #mxnet #neural_networks #pytorch #time_series #time_series_forecasting #time_series_prediction
https://github.com/awslabs/gluon-ts
https://github.com/awslabs/gluon-ts
GitHub
GitHub - awslabs/gluonts: Probabilistic time series modeling in Python
Probabilistic time series modeling in Python. Contribute to awslabs/gluonts development by creating an account on GitHub.
#python #age_estimation #arcface #face_alignment #face_detection #face_recognition #mxnet #oneflow #paddlepaddle #pytorch #retinaface
InsightFace is an open-source toolbox for 2D and 3D face analysis, using PyTorch and MXNet. It offers advanced algorithms for face recognition, detection, and alignment, optimized for both training and deployment. The project includes pre-trained models, various network backbones, and support for multiple operating systems. You can use it for non-commercial research purposes under the MIT License. Key features include integrated face-swapping models that outperform many commercial products, a cross-platform face recognition SDK, and participation in several challenges where InsightFace has achieved top rankings. This tool benefits users by providing robust and efficient face analysis capabilities with easy integration into their projects.
https://github.com/deepinsight/insightface
InsightFace is an open-source toolbox for 2D and 3D face analysis, using PyTorch and MXNet. It offers advanced algorithms for face recognition, detection, and alignment, optimized for both training and deployment. The project includes pre-trained models, various network backbones, and support for multiple operating systems. You can use it for non-commercial research purposes under the MIT License. Key features include integrated face-swapping models that outperform many commercial products, a cross-platform face recognition SDK, and participation in several challenges where InsightFace has achieved top rankings. This tool benefits users by providing robust and efficient face analysis capabilities with easy integration into their projects.
https://github.com/deepinsight/insightface
GitHub
GitHub - deepinsight/insightface: State-of-the-art 2D and 3D Face Analysis Project
State-of-the-art 2D and 3D Face Analysis Project. Contribute to deepinsight/insightface development by creating an account on GitHub.
#jupyter_notebook #computer_vision #deep_learning #drug_discovery #forecasting #large_language_models #mxnet #nlp #paddlepaddle #pytorch #recommender_systems #speech_recognition #speech_synthesis #tensorflow #tensorflow2 #translation
This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.
https://github.com/NVIDIA/DeepLearningExamples
This repository provides top-quality deep learning examples that are easy to train and deploy on NVIDIA GPUs. It includes a wide range of models for computer vision, natural language processing, recommender systems, speech to text, and more. These examples are updated monthly and come in Docker containers with the latest NVIDIA software, ensuring the best performance. The models support multiple GPUs and nodes, and some are optimized for Tensor Cores, which can significantly speed up training. This makes it easier for users to achieve high accuracy and performance in their deep learning projects.
https://github.com/NVIDIA/DeepLearningExamples
GitHub
GitHub - NVIDIA/DeepLearningExamples: State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with…
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure. - NVIDIA/DeepLearningExamples