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.

  1. Classify cancer using simulated data (Logistic Regression)
    CNTK 101:Logistic Regression with NumPy (source)
  2. Classify cancer using simulated data (Feed Forward, FFN)
    CNTK 102: Feed Forward network with NumPy (source)
  3. Recognize hand written digits (OCR) with MNIST data
    CNTK 103 Part A: MNIST data preparation (source), Part B: Multi-class logistic regression classifier (source) Part C: Multi-layer perceptron classifier (source) Part D: Convolutional neural network classifier (source)
  4. Learn how to predict the stock market
    CNTK 104: Time Series basics with finance data (source with finance data)
  5. Compress (using autoencoder) hand written digits from MNIST data with no human input (unsupervised learning, FFN)
    CNTK 105 Part A: MNIST data preparation (source), Part B: Feed Forward autoencoder (source)
  6. Forecasting using data from an IOT device
    CNTK 106: LSTM based forecasting - Part A: with simulated data (source), Part B: with real IOT data (source)
  7. Quick tour for those familiar with other deep learning toolkits
    CNTK 200: Guided Tour (source)
  8. Recognize objects in images from CIFAR-10 data (Convolutional Network, CNN)
    CNTK 201 Part A: CIFAR data preparation (source), Part B: VGG and ResNet classifiers (source)
  9. Infer meaning from text snippets using LSTMs and word embeddings
    CNTK 202: Language understanding (source)
  10. Train a computer to perform tasks optimally (e.g., win games) in a simulated environment
    CNTK 203: Reinforcement learning basics with OpenAI Gym data (source)
  11. Translate text from one domain (grapheme) to other (phoneme)
    CNTK 204: Sequence to sequence basics with CMU pronouncing dictionary (source)
  12. Teach a computer to paint like Picasso or van Gogh
    CNTK 205: Artistic Style Transfer (source)
  13. Produce realistic images with no human input (unsupervised learning)
    CNTK 206 Part A: MNIST data preparation (source), Part B: Basic Generative Adversarial Networks (GAN) (source), Part C: Deep Convolutional GAN (source) Part D: Wasserstein GAN and Loss Sensitive GAN (source)
  14. Training with Sampled Softmax
    CNTK 207: Training with Sampled Softmax (source)
  15. Training with Connectionist Temporal Classification
    CNTK 208: Training with Connectionist Temporal Classification (source)
  16. Recognize flowers and animals in natural scene images using deep transfer learning
    CNTK 301: Deep transfer learning with pre-trained ResNet model (source)
  17. Generate higher resolution images from low resolution ones
    CNTK 302 Part A : Use pre-trained models for generating super-resolution images (source), Part B: Train super resolution models using CNNs and GANs (source)
  18. Compare the similarity between a pair of documents
    CNTK 303: Deep structured semantic modeling with LSTM (source)

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).