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Lab: Deep Learning with Neural Networks and the Cognitive Toolkit (CNTK)

Overview

In this lab, you will learn how to upload data to an Azure storage account, use Python to clean data in Azure storage, use Machine Learning Workbench to execute code in Docker containers, format data for use with the CNTK, use Python code to train CNTK models, determine the accuracy of a trained CNTK model, operationalize a Docker image to containing a trained neural network, and invoke the neural network within the Docker container to get real-time predictions.




Details
  • Estimated time required to complete: 2 hours, 30 minutes
  • You will have access to this environment for 5 hours, 0 minutes
  • Learning Credits Required: 10
Who this lab is designed for
  • Data Professionals
  • Data Scientists
  • Data Developers

Exercises

Exercise 1: Configure Azure ML Workbench and Upload Data to Azure Storage
In this exercise, you will setup Azure ML Workbench on your LabVM. You will then upload a dataset to Azure blob storage. The dataset that you will upload is the MNIST database, which is a popular dataset for training and evaluating handwriting-recognition models. The database contains 60,000 scanned and normalized images of the digits 0 through 9 drawn by high school students. It also includes a set of 10,000 test images for evaluating a model’s accuracy. In subsequent exercises, you will create a neural network and use the MNIST dataset to train it to recognize handwritten digits.
Exercise 2: Preparing Data with Azure Machine Learning Workbench
In this exercise, you will prepare the data to be used in a machine-learning model by converting it into a format supported by the Microsoft Cognitive Toolkit, also known as CNTK. You will use Microsoft’s Azure Machine Learning Workbench, a free cross-platform tool for wrangling data and building machine-learning models, to do the conversion.
Exercise 3: Building Neural Networks with the Microsoft Cognitive Toolkit (CNTK)
In this exercise, you will return to Machine Learning Workbench and train three machine-learning models that rely on CNTK neural networks. The goal: to find the best model for recognizing hand-written digits, with an eye toward operationalizing the model and building a client app that uses it in the fourth and final exercise.
Exercise 4: Operationalizing Machine Learning Models using Docker
In this exercise, you will build a Docker image containing one of the compiled networks. The container will also include a rudimentary Web server written in Node.js that serves up a Web page in which users can sketch digits. A button click submits a digit to the neural network, which “predicts” which digit was drawn, providing a tangible demonstration of machine learning in action.

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Benefits
Real Time Labs allow you to learn technology in an isolated environment without the hassle or cost of setting up a dedicated learning environment.

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