ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
machine-learning
deep-learning
pytorch
tensorflow
neural-networks
scikit-learn
ai-framework
hardware-acceleration
onnx
Обновлено 2024-09-16 01:31:55 +03:00
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
unity
machine-learning
unity3d
deep-learning
reinforcement-learning
neural-networks
deep-reinforcement-learning
Обновлено 2024-09-14 20:00:24 +03:00
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
machine-learning
deep-learning
tensorflow
neural-networks
embedded
speech-recognition
deepspeech
offline
on-device
speech-to-text
Обновлено 2024-09-04 00:17:43 +03:00
Hummingbird compiles trained ML models into tensor computation for faster inference.
Обновлено 2024-08-23 12:06:25 +03:00
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
Обновлено 2023-11-16 20:25:07 +03:00
Datasets, tools, and benchmarks for representation learning of code.
machine-learning
deep-learning
data-science
ml
python
tensorflow
neural-networks
open-data
datasets
cnn
machine-learning-on-source-code
natural-language-processing
nlp
nlp-machine-learning
bert
programming-language-theory
representation-learning
rnn
self-attention
data
Обновлено 2022-01-31 12:25:07 +03:00
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
machine-learning
deep-learning
computer-vision
neural-networks
image-classification
azure-computer-vision
clarifai
google-cloud-vision
provable-defense
adversarial-defense
adversarial-examples
adversarial-robustness
aws-rekognition
Обновлено 2021-04-03 00:37:13 +03:00
Deep Learning for Seismic Imaging and Interpretation
microsoft
deep-learning
computer-vision
neural-networks
segmentation
seismic-processing
seismic
seismic-data
seismic-imaging
seismic-inversion
Обновлено 2020-09-19 01:18:20 +03:00
Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
microsoft
azure-storage
image-classification
neural-networks
cntk
microsoft-azure
land-cover
land-use
geospatial-data
image-segmentation
microsoft-machine-learning
azure-batchai
cntk-model
geospatial-analysis
Обновлено 2019-07-25 06:53:28 +03:00