Using the Deep Learning Virtual Machine
Once you have provisioned your Deep Learning Virtual Machine (DLVM), you can start building deep neural network models to build AI applications in domains like computer vision, language understanding.
There are a number of tools provided on the DLVM for AI. The Deep Learning and AI frameworks page contains details on how to use these tools. The samples page provides pointers to the code examples pre-loaded on the VM for each of the frameworks to help you get started quickly.
Deep Learning tutorials and walkthroughs
In addition to the framework-based samples, we also provide a set of comprehensive walkthroughs that have been validated on the DLVM. These help you jump-start your development of deep learning applications in domains like image and text/language understanding. We continue to add more end-to-end tutorials across different domains and technology.
Running neural networks across different frameworks: This is a comprehensive walkthrough that shows how to migrate code from one framework to another. It also demonstrates how to compare model and run time performance across frameworks.
A how-to guide to build an end-to-end solution to detect products within images: Image detection is a technique that can locate and classify objects within images. This technology has the potential to bring huge rewards in many real life business domains. For example, retailers can use this technique to determine which product a customer has picked up from the shelf. This information in turn helps stores manage product inventory.
Classification of text documents: This walkthrough demonstrates how to build and train two different neural network architectures (Hierarchical Attention Network and Long Short Term Memory (LSTM) network) for classification of text documents using the Keras API for deep learning. Keras is a front end to three of the most popular deep learning frameworks, Microsoft Cognitive Toolkit, TensorFlow, and Theano.