Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
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Updated
Mar 11, 2023 - C++
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
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.
Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
Setup and customize deep learning environment in seconds.
Scenarios, tutorials and demos for Autonomous Driving
Keras package for region-based convolutional neural networks (RCNNs)
GPU-accelerated Deep Learning on Windows 10 native
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.
Deep Learning with C# and CNTK
ANNdotNET - deep learning tool on .NET Platform.
A Deep Learning talk+tutorial for medical image processing
A workbench for online model-free Reinforcement Learning on continuous control problems
This POC is using CNTK 2.1 to train model for multiclass classification of images. Our model is able to recognize specific objects (i.e. toilet, tap, sink, bed, lamp, pillow) connected with picture types we are looking for. It plays a big role in a process which will be used to classify pictures from different hotels and determine whether it's a…
A python implementation for a CNTK Fast-RCNN evaluation client
Bounding box detection of drones (small scale quadcopters) with CNTK Fast R-CNN
The On-Ramp to Deep Learning
We provide GPU-enabled docker images including Keras, TensorFlow, CNTK, MXNET and Theano.
This sample project shows off how to prepare and deploy to Azure Web Apps a simple Python web service with an image classifying model produced in CNTK (Cognitive Toolkit) using FasterRCNN
Machine Comprehension Train on MSMARCO with S-NET Extraction Modification
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