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