Revision a2055f6e3d624543e654c4a1565e91557dc9bf47 authored by liqun fu on 27 March 2020, 21:40:05 UTC, committed by liqun fu on 27 March 2020, 21:40:05 UTC
1 parent 80e9f79
samples.json
[
{
"category": ["GettingStarted"],
"name": "LogisticRegression_FunctionalAPI",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/1stSteps/LogisticRegression_FunctionalAPI.py",
"description": "Introduces basic CNTK usage using logistic regression as an example, using the Functional API.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "5/24/2017"
},
{
"category": ["GettingStarted"],
"name": "LogisticRegression_GraphAPI",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/1stSteps/LogisticRegression_GraphAPI.py",
"description": "Introduces basic CNTK usage using logistic regression as an example, using the Graph API.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "5/24/2017"
},
{
"category": [""],
"name": "Model Evaluation on Azure",
"url": "https://docs.microsoft.com/en-us/cognitive-toolkit/Evaluate-a-model-in-an-Azure-WebApi",
"description": "Host an already trained CNTK model on Azure.",
"language": [],
"type": ["Tutorial"],
"dateadded": "4/14/2017"
},
{
"category": [""],
"name": "Model Evaluation in C++/C#/.NET",
"url": "https://docs.microsoft.com/en-us/cognitive-toolkit/CNTK-Eval-Examples",
"description": "Evaluation or inference of an already trained CNTK model in C++/C#.",
"language": ["C++", "C#", ".NET"],
"type": ["Example"],
"dateadded": "4/14/2017"
},
{
"category": [""],
"name": "Model Evaluation on Universal Windows Platform (UWP)",
"url": "https://docs.microsoft.com/en-us/cognitive-toolkit/CNTK-Library-Evaluation-on-UWP",
"description": "Evaluation or inference of an already trained CNTK model on UWP.",
"language": ["C++", "C#"],
"type": ["Example"],
"dateadded": "7/31/2017"
},
{
"category": ["Image"],
"name": "Transfer Learning",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/TransferLearning",
"description": "Transfer Learning: adapt a pretrained model to a new classification task.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Transfer Learning",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_301_Image_Recognition_with_Deep_Transfer_Learning.ipynb",
"description": "Recognize flowers and animals in natural scene images using deep transfer learning with pre-trained ResNet data.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Image auto encoder",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#07_deconvolution",
"description": "A simple image auto encoder using deconvolution and unpooling.",
"language": ["BrainScript", "Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Feature Extraction",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/FeatureExtraction",
"description": "Evaluate and write out different layers of a trained model using Python.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Video Action Recognition",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Video/GettingStarted",
"description": "Train a Basic 3-D convolutional neural network for video action recognition on the UCF11 dataset.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "AlexNet",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/AlexNet",
"description": "CNN based network by Alex Krizhevsky. This was the winning model of the ILSVRC2012 classification task.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "ConvNet",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/ConvNet#cntk-examples-imageclassificationconvnet",
"description": "A popular convolutional neural network for image-related tasks.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Faster R-CNN",
"url": "https://docs.microsoft.com/en-us/cognitive-toolkit/Object-Detection-using-Faster-R-CNN",
"description": "Train object detection from images using a region proposal netwrok in Faster R-CNN.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "8/30/2017"
},
{
"category": ["Image"],
"name": "Fast R-CNN",
"url": "https://docs.microsoft.com/en-us/cognitive-toolkit/Object-Detection-using-Fast-R-CNN",
"description": "Train object detection from images by adapting pre-trained classification models on arbitrarily sized regions of interest using ROI pooling.",
"language": ["Python", "BrainScript"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Faster R-CNN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FasterRCNN",
"description": "Train object detection from images by adapting pre-trained classification models on arbitrarily sized regions of interest using ROI pooling and region proposal networks.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "7/25/2017"
},
{
"category": ["Image"],
"name": "GoogLeNet (BN-Inception)",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/GoogLeNet/BN-Inception#cntk-examples-imageclassificationgooglenetbn-inception",
"description": "GoogLeNet (BN-Inception) network for image classification.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "GoogLeNet (Inception V3)",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/GoogLeNet/InceptionV3#cntk-examples-imageclassificationgooglenetinceptionv3",
"description": "GoogLeNet (Inception V3) network for image classification.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "GoogLeNet (Inception-ResNet-V1)",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/GoogLeNet/Inception-ResNet-V1#cntk-examples-imageclassificationgooglenetinception-resnet-v1",
"description": "GoogLeNet (Inception-ResNet-V1) network for image classification.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "7/5/2017"
},
{
"category": ["Image"],
"name": "MNIST Logistic Regression (LR) OCR",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103B_MNIST_LogisticRegression.ipynb",
"description": "Recognize hand written digits (OCR) with MNIST data using multi-class LR.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/26/2017"
},
{
"category": ["Image"],
"name": "MNIST Multi-Layer Perceptron (MLP) OCR",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103C_MNIST_MultiLayerPerceptron.ipynb",
"description": "Recognize hand written digits (OCR) with MNIST data using MLP.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/24/2017"
},
{
"category": ["Image"],
"name": "MNIST Convolutional Neural Network (CNN) OCR",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_103D_MNIST_ConvolutionalNeuralNetwork.ipynb",
"description": "Recognize hand written digits (OCR) with MNIST data using CNN.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "5/1/2017"
},
{
"category": ["Image"],
"name": "MNIST Autoencoder Dim Reduction",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.ipynb",
"description": "Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning).",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Image Recognition",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb",
"description": "Hands-on lab that shows how to implement an image recognition task using convolutional neural networks.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Neural Artistic Style Transfer",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb",
"description": "This tutorial shows how to transfer the style of one image to another. This allows us to take our ordinary photos and render them in the style of famous images or paintings.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Generative Adversarial Networks (GAN)",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206A_Basic_GAN.ipynb",
"description": "This tutorial is a basic implementation of GAN networks. This allows us to generate realistic looking MNIST images with no human input (unsupervised learning).",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Deep Convolutional Generative Adversarial Networks (DCGAN)",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206B_DCGAN.ipynb",
"description": "This tutorial is an implementation of deep convolutional GAN (DCGAN) networks. This allows us to generate realistic looking MNIST images with no human input (unsupervised learning).",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Wasserstein and Loss Sensitive Generative Adversarial Networks (WGAN and LSGAN)",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206C_WGAN_LSGAN.ipynb",
"description": "This tutorial is an implementation of Wasserstein and Loss Sensitive GAN networks. This allows us to generate CIFAR images with no human input (unsupervised learning).",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "10/17/2017"
},
{
"category": ["Image"],
"name": "One Conv",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#02_oneconvcntk",
"description": "Simple network with one convolutional layer.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "One Conv BN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#04_oneconvbncntk",
"description": "Simple network showing how to add batch normalization to a CNN.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "One Conv Dropout",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#03_oneconvdropoutcntk",
"description": "Simple network showing how to add dropout to a CNN.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "One Hidden",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#01_onehiddencntk",
"description": "Simple network with one hidden layer.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "MNIST Regression",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/GettingStarted#05_oneconvregrcntk",
"description": "Simple network showing how to treat MNIST as a regression problem using Root Mean Square Error.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "Simple Regression",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Regression#cntk-examples-imageregression",
"description": "A simple neural network to predict the average RGB values of normalized images.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "ResNet",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/ResNet#cntk-examples-imageclassificationresnet",
"description": "Deep residual learning invented by Microsoft Research. This was the winning model of the ILSVRC and MS-COCO challenges in 2015.",
"language": ["Python", "BrainScript"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Image"],
"name": "VGG",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Classification/VGG",
"description": "Deep CNN from University of Oxford. This was the winning model for the ILSVRC2014 localization task.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "Feed Forward Classification",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_102_FeedForward.ipynb",
"description": "Classify cancer using simulated data (Feed Forward) with Numpy",
"language": ["Python"],
"type": ["Tutorial"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "Finance Timeseries with CNTK, Pandas and Numpy",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb",
"description": ": Looking at the predictive potential on classification of an Exchange-traded Funds (ETF), and in this simplified setting how one could trade it.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "Logistic Regression",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_101_LogisticRegression.ipynb",
"description": "Classify cancer using simulated data (Logistic Regression).",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "LSTM based Timeseries with Simulated Data",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb",
"description": "Timeseries modeling with LSTMs using simulated data.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "LSTM based Timeseries with real world data",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_106B_LSTM_Timeseries_with_IOT_Data.ipynb",
"description": "Forecasting using data from an IOT device.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "Reinforcement Learning",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb",
"description": "Example how software agents could take actions in an environment so as to maximize some notion of cumulative reward.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Numeric"],
"name": "Training with Sampled Softmax",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb",
"description": "Training with Sampled Softmax",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "AN4 Speech DNN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/AN4",
"description": "Train a speech recognition DNN acoustic model on the CMU AN4 dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "AN4 Speech LSTM",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/AN4",
"description": "Train a speech recognition LSTM acoustic model on the CMU AN4 dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Kaldi Speech DNN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/AMI",
"description": "Train a speech recognition DNN acoustic model on top of fMLLR features generated with the Kaldi toolchain.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Speech Adapt Learn Rate",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train a speech recognition model with learning rate adapted based on dev set on the TIMIT dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Speech Auto Encoder",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train autoencoder with bottleneck layer on the TIMIT dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Speech Multi Input",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train with two different inputs, fbank and mfcc, on the TIMIT dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Speech Multi Task",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train with multi-task learning and joint prediction of senone labels and dialect region on the TIMIT dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Speech with PreTrain",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Pre-train using layerwise discriminative pre-training, then full network training on the TIMIT dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "TIMIT Speech DNN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train a speech recognition DNN acoustic model on the TIMIT dataset (TrainSimpleNetwork).",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "TIMIT Speech LSTM",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Speech/Miscellaneous/TIMIT/config",
"description": "Train a speech recognition LSTM acoustic model on the TIMIT dataset (TrainNDLNetwork).",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech"],
"name": "Connectionist Temporal Classification",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_208_Speech_Connectionist_Temporal_Classification.ipynb",
"description": "Train a speech recognition LSTM acoustic model with CTC criteria.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "5/20/2017"
},
{
"category": ["Speech", "Text"],
"name": "Grapheme to Phoneme (G2P)",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/SequenceToSequence/CMUDict",
"description": "Sequence-to-sequence model with attention mechanism for a grapheme to phoneme translation task on the CMUDict dataset.",
"language": ["Python", "BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Speech", "Text"],
"name": "Sequence-to-Sequence",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb",
"description": "Translate text from one domain (grapheme) to other (phoneme). The input is a sequence with a dynamic length, and the output is also a sequence with some dynamic length. This is a natural fit for machine translation , automatic text summarization , word to pronunciation models and even parse tree generation.",
"language": ["Python"],
"type": ["Tutorial"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "LTSM LU on ATIS",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/LanguageUnderstanding/ATIS",
"description": "LSTM based model for language understanding on the ATIS dataset.",
"language": ["Python", "BrainScript"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "Language Understanding with Recurrent Networks",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_202_Language_Understanding.ipynb",
"description": "Implement a recurrent network for language understanding of the ATIS dataset.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "4/14/2017"
},
{
"category": [ "Text" ],
"name": "Neural Character Language Model",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Text/CharacterLM",
"description": "A neural language model uses a recurrent neural network to predict words (or characters) with a richer context than traditional n-gram models allow.",
"language": [ "Python" ],
"type": [ "Recipe" ],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "Word Based Language Model using Sampled Softmax",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Text/WordLMWithSampledSoftmax",
"description": "An example on how to use sampled softmax to train a neural language model.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "RNN LM on PennTreebank",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/SequenceToSequence/PennTreebank",
"description": "Recurrent neural network for language modeling on the PennTreebank dataset.",
"language": ["BrainScript"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "Sequence Classification",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/SequenceClassification/SimpleExample/Python",
"description": "Create and train an LSTM sequence classification model.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "Training with Sampled Softmax",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_207_Training_with_Sampled_Softmax.ipynb",
"description": "Improve training speed for models with large vocabularies.",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "4/14/2017"
},
{
"category": ["Text"],
"name": "LightRNN",
"url": "https://github.com/Microsoft/CNTK/tree/master/Examples/Text/LightRNN",
"description": "LightRNN: Memory and Computation-Efficient Recurrent Neural Networks",
"language": ["Python"],
"type": ["Recipe"],
"dateadded": "07/14/2017"
},
{
"category": ["Reinforcement Learning"],
"name": "Deep Q Neural Network",
"url":"https://github.com/Microsoft/CNTK/tree/master/Examples/ReinforcementLearning",
"description": "Train an agent to play Atari Games using Reinforcement Learning",
"language": ["Python"],
"type": ["Recipe"],
"dataadded": "05/05/2017"
},
{
"category": ["Reinforcement Learning"],
"name": "Flapping Bird with Keras",
"url":"https://github.com/Microsoft/CNTK/tree/master/Examples/ReinforcementLearning/FlappingBirdWithKeras",
"description": "Using CNTK Keras backend to train an agent to navigate a bird through a cactus maze",
"language": ["Python"],
"type": ["Recipe"],
"dataadded": "07/21/2017"
},
{
"category": ["Text"],
"name": "Deep semantic modeling",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_303_Deep_Structured_Semantic_Modeling_with_LSTM_Networks.ipynb",
"description": "Compare two documents using deep semantic similarity.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "10/11/2017"
},
{
"category": ["Image"],
"name": "Pretrained Super-resolution GANs",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_302A_Evaluation_of_Pretrained_Super-resolution_Models.ipynb",
"description": "This tutorial uses pre-trained models for generating high resolution images from lower resolution images.",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "10/17/2017"
},
{
"category": ["Image"],
"name": "Training super-resolution GANs",
"url": "https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_302B_Image_Super-resolution_Using_CNNs_and_GANs.ipynb",
"description": "This tutorial shows how to train networks to generate higher resolution images using CNNs and GANs",
"language": ["Python"],
"type": ["Tutorial", "Recipe"],
"dateadded": "10/17/2017"
}
]
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