Tutorials
=======================================================

*For a quick tour if you are familiar with another deep learning toolkit please fast forward to CNTK 200 (A guided tour) for a range of constructs to train and evaluate models using CNTK.*

#.  *Classify cancer using simulated data (Logistic Regression)*
     CNTK 101:`Logistic Regression <CNTK_101_LogisticRegression.html>`_ with NumPy (:cntktut:`source <CNTK_101_LogisticRegression>`)
    
    
#.  *Classify cancer using simulated data (Feed Forward, FFN)*
     CNTK 102: `Feed Forward network <CNTK_102_FeedForward.html>`_  with NumPy (:cntktut:`source <CNTK_102_FeedForward>`)
     
#.  *Recognize hand written digits (OCR) with MNIST data*
     CNTK 103 Part A: `MNIST data preparation <CNTK_103A_MNIST_DataLoader.html>`_ (:cntktut:`source <CNTK_103A_MNIST_DataLoader>`),
     Part B: `Multi-class logistic regression classifier <CNTK_103B_MNIST_LogisticRegression.html>`_ (:cntktut:`source <CNTK_103B_MNIST_LogisticRegression>`)
     Part C: `Multi-layer perceptron classifier <CNTK_103C_MNIST_MultiLayerPerceptron.html>`_
     (:cntktut:`source <CNTK_103C_MNIST_MultiLayerPerceptron>`)
     Part D: `Convolutional neural network classifier <CNTK_103D_MNIST_ConvolutionalNeuralNetwork.html>`_ (:cntktut:`source <CNTK_103D_MNIST_ConvolutionalNeuralNetwork>`)

#.  *Learn how to predict the stock market*
     CNTK 104: `Time Series basics <CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.html>`_ with finance data (:cntktut:`source <CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy>` with finance data)

#.  *Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning, FFN)*
     CNTK 105 Part A: `MNIST data preparation <CNTK_103A_MNIST_DataLoader.html>`_ (:cntktut:`source <CNTK_103A_MNIST_DataLoader>`),
     Part B: `Feed Forward autoencoder <CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction.html>`_ (:cntktut:`source <CNTK_105_Basic_Autoencoder_for_Dimensionality_Reduction>`)

#.  *Forecasting using data from an IOT device*
     CNTK 106: LSTM based forecasting - Part A: `with simulated data <CNTK_106A_LSTM_Timeseries_with_Simulated_Data.html>`_ (:cntktut:`source <CNTK_106A_LSTM_Timeseries_with_Simulated_Data>`),
     Part B: `with real IOT data <CNTK_106B_LSTM_Timeseries_with_IOT_Data.html>`_ (:cntktut:`source <CNTK_106B_LSTM_Timeseries_with_IOT_Data>`)

#.  *Quick tour for those familiar with other deep learning toolkits*
     CNTK 200: `Guided Tour <CNTK_200_GuidedTour.html>`_ (:cntktut:`source <CNTK_200_GuidedTour>`)

#.  *Recognize objects in images from CIFAR-10 data (Convolutional Network, CNN)*
     CNTK 201 Part A: `CIFAR data preparation <CNTK_201A_CIFAR-10_DataLoader.html>`_ (:cntktut:`source <CNTK_201A_CIFAR-10_DataLoader>`),
     Part B: `VGG and ResNet classifiers <CNTK_201B_CIFAR-10_ImageHandsOn.html>`_ (:cntktut:`source <CNTK_201B_CIFAR-10_ImageHandsOn>`)

#.  *Infer meaning from text snippets using LSTMs and word embeddings*
     CNTK 202: `Language understanding <CNTK_202_Language_Understanding.html>`_ (:cntktut:`source <CNTK_202_Language_Understanding>`)

#.  *Train a computer to perform tasks optimally (e.g., win games) in a simulated environment*
     CNTK 203: `Reinforcement learning basics <CNTK_203_Reinforcement_Learning_Basics.html>`_ with OpenAI Gym data (:cntktut:`source <CNTK_203_Reinforcement_Learning_Basics>`)

#.  *Translate text from one domain (grapheme) to other (phoneme)*
     CNTK 204: `Sequence to sequence basics <CNTK_204_Sequence_To_Sequence.html>`_ with CMU pronouncing dictionary (:cntktut:`source <CNTK_204_Sequence_To_Sequence>`)

#.  *Teach a computer to paint like Picasso or van Gogh*
     CNTK 205: `Artistic Style Transfer <CNTK_205_Artistic_Style_Transfer.html>`_ (:cntktut:`source <CNTK_205_Artistic_Style_Transfer>`)

#.  *Produce realistic images with no human input (unsupervised learning)*
     CNTK 206 Part A:  `MNIST data preparation <CNTK_103A_MNIST_DataLoader.html>`_ (:cntktut:`source <CNTK_103A_MNIST_DataLoader>`),
     Part B: `Basic Generative Adversarial Networks (GAN) <CNTK_206A_Basic_GAN.html>`_ (:cntktut:`source <CNTK_206A_Basic_GAN>`),
     Part C: `Deep Convolutional GAN <CNTK_206B_DCGAN.html>`_ (:cntktut:`source <CNTK_206B_DCGAN>`)
     Part D: `Wasserstein GAN and Loss Sensitive GAN <CNTK_206C_WGAN_LSGAN.html>`_ (:cntktut:`source <CNTK_206C_WGAN_LSGAN>`)

#.  *Training with Sampled Softmax*
     CNTK 207: `Training with Sampled Softmax <CNTK_207_Training_with_Sampled_Softmax.html>`_ (:cntktut:`source <CNTK_207_Training_with_Sampled_Softmax>`)

#.  *Training with Connectionist Temporal Classification*
     CNTK 208: `Training with Connectionist Temporal Classification <CNTK_208_Speech_Connectionist_Temporal_Classification.html>`_ (:cntktut:`source <CNTK_208_Speech_Connectionist_Temporal_Classification>`)

#.  *Recognize flowers and animals in natural scene images using deep transfer learning*
     CNTK 301: `Deep transfer learning with pre-trained ResNet model <CNTK_301_Image_Recognition_with_Deep_Transfer_Learning.html>`_ (:cntktut:`source <CNTK_301_Image_Recognition_with_Deep_Transfer_Learning>`)    
     
#.  *Generate higher resolution images from low resolution ones*
     CNTK 302 Part A : `Use pre-trained models for generating super-resolution images <CNTK_301A_Evaluation_of_Pretrained_Super-resolution_Models.html>`_ (:cntktut:`source <CNTK_301A_Evaluation_of_Pretrained_Super-resolution_Models>`),
     Part B: `Train super resolution models using CNNs and GANs <CNTK_302B_Image_Super-resolution_Using_CNNs_and_GANs.html>`_ (:cntktut:`source <CNTK_302B_Image_Super-resolution_Using_CNNs_and_GANs>`)     
     
#.  *Compare the similarity between a pair of documents*
     CNTK 303: `Deep structured semantic modeling with LSTM <CNTK_303_Deep_Structured_Semantic_Modeling_with_LSTM_Networks.html>`_ (:cntktut:`source <CNTK_303_Deep_Structured_Semantic_Modeling_with_LSTM_Networks>`)   

Try these notebooks pre-installed on `CNTK Azure Notebooks`_ for free.

For our Japanese users, you can find some of the `tutorials in Japanese`_ (unsupported).

.. _`CNTK Azure Notebooks`: https://notebooks.azure.com/cntk/libraries/tutorials
.. _`tutorials in Japanese`: https://notebooks.azure.com/library/cntkbeta2_ja

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   CNTK_*