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

.. toctree:: :glob: :maxdepth: 1 :caption: List view :hidden:

CNTK_*