An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
machine-learning
deep-learning
data-science
python
tensorflow
mlops
pytorch
hyperparameter-optimization
hyperparameter-tuning
machine-learning-algorithms
model-compression
nas
neural-architecture-search
neural-network
automated-machine-learning
automl
bayesian-optimization
deep-neural-network
distributed
feature-engineering
Обновлено 2024-07-03 13:54:08 +03:00
The object detection solution accelerator provides a pre-packaged solution to train, deploy and monitor custom object detection models using the TensorFlow object detection API within Azure ML.
Обновлено 2024-01-24 22:16:23 +03:00
TensorFlow 2 library implementing Graph Neural Networks
Обновлено 2023-07-13 16:47:00 +03:00
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Обновлено 2023-06-13 00:30:31 +03:00
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Обновлено 2023-06-13 00:30:31 +03:00
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019)
Обновлено 2022-11-28 22:10:22 +03:00
TensorFlow implementations of Graph Neural Networks
Обновлено 2022-11-28 22:09:39 +03:00
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Обновлено 2022-09-23 02:59:07 +03:00
👩🔬 Train and Serve TensorFlow Models at Scale with Kubernetes and Kubeflow on Azure
machine-learning
docker
kubernetes
tensorflow
jupyter-notebook
distributed-tensorflow
jupyterhub
kubeflow
tensorflow-serving
Обновлено 2020-11-13 20:49:23 +03:00
Distributed Deep Learning using AzureML
Обновлено 2019-11-19 05:28:26 +03:00
Walkthrough demonstrating how trained DNNs (CNTK and TensorFlow) can be applied to massive image sets in ADLS using PySpark on Azure HDInsight clusters
Обновлено 2017-09-06 22:46:02 +03:00